phd proposal machine learning

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• AE 8803 Gaussian Processes
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6402 , Time Series Analysis
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6221 , Probability Theory for Scientists and Engineers
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• AE 8803 , Optimal Transport Theory and Applications
• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6241 , Probability I
• MATH 6262 , Advanced Statistical Inference
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 6790 / ECE 6790 , Information Processing Models in Neural Systems
• BMED 7610 , Quantitative Neuroscience
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CHBE 6745 , Data Analytics for Chemical Engineers
• CHBE 6746 , Data-Driven Process Engineering
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7647 , Machine Learning with Limited Supervision
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• AE 6513 , Mathematical Planning and Decision-Making for Autonomy
• AE 8803 , Optimization-Based Learning Control and Games
• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ECE 6553 , Optimal Control and Optimization
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Thesis Proposal

In the thesis proposal, the PhD or DES student lays out an intended course of research for the dissertation.  By accepting the thesis proposal, the student’s dissertation proposal committee agrees that the proposal is practicable and acceptable, that its plan and prospectus are satisfactory, and that the candidate is competent in the knowledge and techniques required, and formally recommends that the candidate proceed according to the prospectus and under the supervision of the dissertation committee. It is part of the training of the student’s research apprenticeship that the form of this proposal must be as concise as those proposals required by major funding agencies.

The student proposes to a committee consisting of the student’s advisor and two other researchers who meet requirements for dissertation committee membership.  The advisor should solicit the prospective committee members, not the student. In cases where the research and departmental advisors are different , both must serve on the committee.

The student prepares a proposal document that consists of a core, plus any optional appendices. The core is limited to 30 pages (e.g., 12 point font, single spacing, 1 inch margins all around), and should contain sections describing 1) the problem and its background, 2) the innovative claims of the proposed work and its relation to existing work, 3) a description of at least one initial result that is mature enough to be able to be written up for submission to a conference, and 4) a plan for completion of the research. The committee commits to read and respond to the core, but reserves the right to refuse a document whose core exceeds the page limit. The student cannot assume that the committee will read or respond to any additional appendices.

The complete doctoral thesis proposal document must be disseminated to the entire dissertation committee no later than two weeks (14 days) prior to the proposal presentation. The PhD Program Administrator must be informed of the scheduling of the proposal presentation no later than two weeks (14 days) prior to the presentation. Emergency exceptions to either of these deadlines can be granted by the Director of Graduate Studies or the Department Chair on appeal by the advisor and agreement of the committee.

A latex thesis proposal template is available here .

PRESENTATION AND FEEDBACK

The student presents the proposal in a prepared talk of 45 minutes to the committee, and responds to any questions and feedback by the committee.

The student’s advisor, upon approval of the full faculty, establishes the target semester by which the thesis proposal must be successfully completed. The target semester must be no later than the eighth semester, and the student must be informed of the target semester no later than the sixth semester.

The candidacy   exam  must be successfully completed  before  the  proposal can be attempted.  The proposal must be completed prior to submitting the application for defense. [Instituted by full faculty vote September 16, 2015.]

Passing or failing is determined by consensus of the committee, who then sign the dissertation proposal form (sent to advisors by phd-advising@cs.  Failure to pass the thesis proposal by the end of the target semester or the eighth semester, whichever comes first, is deemed unsatisfactory progress: the PhD or DES student is normally placed on probation and can be immediately dismissed from the program. However, on appeal of the student’s advisor, one semester’s grace can be granted by the full faculty.

Last updated on October 16, 2023.

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Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan  to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• Developing AI algorithms for early detection of chronic diseases using patient data.
• The use of deep learning in enhancing the accuracy of weather prediction models.
• Machine learning techniques for real-time language translation in social media platforms.
• AI-driven approaches to improve cybersecurity in financial transactions.
• The role of AI in optimizing supply chain logistics for e-commerce.
• Investigating the impact of machine learning in personalized education systems.
• The use of AI in predictive maintenance for industrial machinery.
• Developing ethical frameworks for AI decision-making in healthcare.
• The application of ML algorithms in autonomous vehicle navigation systems.
• AI in agricultural technology: Optimizing crop yield predictions.
• Machine learning techniques for enhancing image recognition in security systems.
• AI-powered chatbots: Improving customer service efficiency in retail.
• The impact of AI on enhancing energy efficiency in smart buildings.
• Deep learning in drug discovery and pharmaceutical research.
• The use of AI in detecting and combating online misinformation.
• Machine learning models for real-time traffic prediction and management.
• AI applications in facial recognition: Privacy and ethical considerations.
• The effectiveness of ML in financial market prediction and analysis.
• Developing AI tools for real-time monitoring of environmental pollution.
• Machine learning for automated content moderation on social platforms.
• The role of AI in enhancing the accuracy of medical diagnostics.
• AI in space exploration: Automated data analysis and interpretation.
• Machine learning techniques in identifying genetic markers for diseases.
• AI-driven personal finance management tools.
• The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

• Machine learning in cybersecurity threat detection and response.
• AI applications in virtual reality and augmented reality experiences.
• Developing ethical AI systems for recruitment and hiring processes.
• Machine learning for sentiment analysis in customer feedback.
• AI in sports analytics for performance enhancement and injury prevention.
• The role of AI in improving urban planning and smart city initiatives.
• Machine learning models for predicting consumer behaviour trends.
• AI and ML in artistic creation: Music, visual arts, and literature.
• The use of AI in automated drone navigation for delivery services.
• Developing AI algorithms for effective waste management and recycling.
• Machine learning in seismology for earthquake prediction.
• AI-powered tools for enhancing online privacy and data protection.
• The application of ML in enhancing speech recognition technologies.
• Investigating the role of AI in mental health assessment and therapy.
• Machine learning for optimization of renewable energy systems.
• AI in fashion: Predicting trends and personalizing customer experiences.
• The impact of AI on legal research and case analysis.
• Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
• Machine learning in genomic data analysis for personalized medicine.
• AI-driven algorithms for credit scoring in microfinance.
• The use of AI in enhancing public safety and emergency response systems.
• Machine learning for improving water quality monitoring and management.
• AI applications in wildlife conservation and habitat monitoring.
• The role of AI in streamlining manufacturing processes.
• Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
• HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
• Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
• Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
• Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
• Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
• Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
• Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
• Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
• Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
• Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
• Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
• Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
• Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
• Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
• Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
• Machine Learning in Tourism (Rugge, 2022)
• Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
• Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Private Coaching service for hands-on support finding the perfect research topic.

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Machine Learning - CMU

PhD Dissertations

[all are .pdf files].

Neural processes underlying cognitive control during language production (unavailable) Tara Pirnia, 2024

The Neurodynamic Basis of Real World Face Perception Arish Alreja, 2024

Towards More Powerful Graph Representation Learning Lingxiao Zhao, 2024

Robust Machine Learning: Detection, Evaluation and Adaptation Under Distribution Shift Saurabh Garg, 2024

UNDERSTANDING, FORMALLY CHARACTERIZING, AND ROBUSTLY HANDLING REAL-WORLD DISTRIBUTION SHIFT Elan Rosenfeld, 2024

Representing Time: Towards Pragmatic Multivariate Time Series Modeling Cristian Ignacio Challu, 2024

Foundations of Multisensory Artificial Intelligence Paul Pu Liang, 2024

Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion Ian Char, 2024

Learning Models that Match Jacob Tyo, 2024

Improving Human Integration across the Machine Learning Pipeline Charvi Rastogi, 2024

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023

METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023

NEURAL REASONING FOR QUESTION ANSWERING Haitian Sun, 2023

Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

Google PhD fellowship program

Google PhD Fellowships directly support graduate students as they pursue their PhD, as well as connect them to a Google Research Mentor.

Nurturing and maintaining strong relations with the academic community is a top priority at Google. The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. Fellowships support promising PhD candidates of all backgrounds who seek to influence the future of technology. Google’s mission is to foster inclusive research communities and encourage people of diverse backgrounds to apply. We currently offer fellowships in Africa, Australia, Canada, East Asia, Europe, India, Latin America, New Zealand, Southeast Asia and the United States.

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Program details

Application status, how to apply, research areas of focus, review criteria, award recipients.

Applications are currently closed.

Update on 2024 Announcement : Decisions for the 2024 application cycle, originally planned for July 2024, will now be announced via email in August 2024. We apologize for the delay and appreciate your patience as we work to finalize decisions.

• Launch March 27, 2024
• Deadline May 8, 2024
• Awardees Notified By Aug. 31, 2024

The details of each Fellowship vary by region. Please see our FAQ for eligibility requirements and application instructions.

PhD students must be nominated by their university. Applications should be submitted by an official representative of the university during the application window. Please see the FAQ for more information.

Australia and New Zealand

Canada and the United States

PhD students in Japan, Korea and Taiwan must be nominated by their university. After the university's nomination is completed, either an official representative of the university or the nominated students can submit applications during the application window. Please see the FAQ for more information.

India and Southeast Asia

PhD students apply directly during the application window. Please see the FAQ for more information.

Latin America

The 2024 application cycle is postponed. Please check back in 2025 for details on future application cycles.

Google PhD Fellowship students are a select group recognized by Google researchers and their institutions as some of the most promising young academics in the world. The Fellowships are awarded to students who represent the future of research in the fields listed below. Note that region-specific research areas will be listed in application forms during the application window.

Algorithms and Theory

Distributed Systems and Parallel Computing

Health and Bioscience

Human-Computer Interaction and Visualization

Machine Intelligence

Machine Perception

Natural Language Processing

Quantum Computing

Security, Privacy and Abuse Prevention

Software Engineering

Software Systems

Speech Processing

Applications are evaluated on the strength of the research proposal, research impact, student academic achievements, and leadership potential. Research proposals are evaluated for innovative concepts that are relevant to Google’s research areas, as well as aspects of robustness and potential impact to the field. Proposals should include the direction and any plans of where your work is going in addition to a comprehensive description of the research you are pursuing.

In Canada and the United States, East Asia and Latin America, essay responses are evaluated in addition to application materials to determine an overall recommendation.

What does the Google PhD Fellowship include?

Students receive named Fellowships which include a monetary award. The funds are given directly to the university to be distributed to cover the student’s expenses and stipend as appropriate. In addition, the student will be matched with a Google Research Mentor. There is no employee relationship between the student and Google as a result of receiving the fellowship. The award does not preclude future eligibility for internships or employment opportunities at Google, nor does it increase the chances of obtaining them. If students wish to apply for a job at Google, they are welcome to apply for jobs and go through the same hiring process as any other person.

• Up to 3 year Fellowship
• US $12K to cover stipend and other research related activities, travel expenses including overseas travel
• Google Research Mentor
• 1 year Fellowship
• AUD $15K to cover stipend and other research related activities, travel expenses including overseas travel
• Up to 2 year Fellowship (effective from 2024 for new recipients)
• Full tuition and fees (enrollment fees, health insurance, books) plus a stipend to be used for living expenses, travel and personal equipment
• US $10K to cover stipend and other research related activities, travel expenses including overseas travel
• Yearly bursary towards stipend / salary, health care, social benefits, tuition and fees, conference travel and personal computing equipment. The bursary varies by country.

Early-stage PhD students

• Up to 4 year Fellowship
• US $50K to cover stipend and other research related activities, travel expenses including overseas travel

Late-stage PhD students

• US $10K to recognise research contributions, cover stipend and other research related activities, travel expenses including overseas travel
• US $15K per year to cover stipend and other research related activities, travel expenses including overseas travel

Southeast Asia

• US $10K per year for up to 3 years (or up to graduation, whichever is earlier) to cover stipend and other research related activities, travel expenses including overseas travel

Is my university eligible for the PhD Fellowship Program?

Africa, Australia/New Zealand , Canada, East Asia, Europe and the United States : universities must be an accredited research institution that awards research degrees to PhD students in computer science (or an adjacent field).

India, Latin America and Southeast Asia : applications are open to universities/institutes in India, Latin America (excluding Cuba), and in eligible Southeast Asian countries/regions (Brunei, Cambodia, Indonesia, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam).

Restrictions : All award payments and recipients will be reviewed for compliance with relevant US and international laws, regulations and policies. Google reserves the right to withhold funding that may violate laws, regulations or our policies.

What are the eligibility requirements for students?

All regions

• Students must remain enrolled full-time in the PhD program for the duration of the Fellowship or forfeit the award.
• Google employees, and their spouses, children, and members of their household are not eligible.
• Students that are already supported by a comparable industry award are not eligible. Government or non-profit organization funding is exempt.
• Past awardees from the PhD Fellowship program are not eligible to apply again.
• Grant of the Fellowship does not mean admission to a PhD program. The awardee must separately apply and be accepted to a PhD program in computer science (or an adjacent field) at an eligible institution.
• Grant of the Fellowship will be subject to the rules and guidelines applicable in the institution where the awardee registers for the PhD program.

Nominated students in Africa, Australia and New Zealand, Canada and the United States, East Asia and Europe.

Universities should only nominate students that meet the following requirements:

• Africa: Incoming PhD students are eligible to apply, but the Fellowship award shall be contingent on the awardee registering for a full-time PhD program in computer science (or an adjacent field) within the academic award year of the Fellowship award, or the award shall be forfeited.
• Australia and New Zealand : early-stage students enrolled in the first or second year of their PhD (no requirement for completion of graduate coursework by the academic award year).
• Canada and the United States : students who have completed graduate coursework in their PhD by the academic award year when the Fellowship begins.
• East Asia: students who have completed most of graduate coursework in their PhD by the academic award year when the Fellowship begins. Students should have sufficient time for research projects after receiving a fellowship.
• Europe: Students enrolled at any stage of their PhD are eligible to apply.

Direct applicant students in India, Latin America and Southeast Asia

• Latin America : incoming or early stage-students enrolled in the first or second year of their PhD (no requirement for completion of graduate coursework by the academic award year).

What should be included in an application? What language should the application be in?

All application materials should be submitted in English.

For each student nomination, the university will be asked to submit the following material in a single, flat (not portfolio) PDF file:

• Student CV with links to website and publications (if available)
• Short (1-page) resume/CV of the student's primary PhD program advisor
• Available transcripts (mark sheets) starting from first year/semester of Bachelor's degree to date
• Research proposal (maximum 3 pages, excluding references)
• 2-3 letters of recommendation from those familiar with the nominee''s work (at least one from the thesis advisor for current PhD students)
• Student essay response (350-word limit) to: What impact would receiving this Fellowship have on your education? Describe any circumstances affecting your need for a Fellowship and what educational goals this Fellowship will enable you to accomplish.
• Transcripts of current and previous academic records
• 1-2 letters of recommendation from those familiar with the nominee's work (at least one from the thesis advisor)

Canada, East Asia, the United States

• Cover sheet signed by the Department Chair confirming the student passes eligibility requirements. (See FAQ "What are the eligibility requirements for students?")
• Short (1-page) CV of the student's primary advisor
• 2-3 letters of recommendation from those familiar with the nominee's work (at least one from the thesis advisor)
• Research / dissertation proposal (maximum 3 pages, excluding references)
• Student essay response (350-word limit) to: Describe the desired impact your research will make on the field and society, and why this is important to you. Include any personal, educational and/or professional experiences that have motivated your research interests.
• Student essay response (350-word limit) to: Describe an example of your leadership experience in which you have positively influenced others, helped resolve disputes or contributed to group efforts over time. (A leadership role can mean more than just a title. It can mean being a mentor to others, acting as the person in charge of a specific task, or taking the lead role in organizing an event or project. Think about what you accomplished and what you learned from the experience. What were your responsibilities? Did you lead a team? How did your experience change your perspective on leading others? Did you help to resolve an important dispute at your school, church, in your community or an organization? And your leadership role doesn’t necessarily have to be limited to school activities. For example, do you help out or take care of your family?)

Students will need the following documents in a single, flat (not portfolio) PDF file in order to complete an application (in English only):

• Student applicant’s resume with links to website and publications (if available)
• Short (one-page) resume/CV of the student applicant's primary PhD program advisor
• 2-3 letters of recommendation from those familiar with the applicant's work (at least one from the thesis advisor for current PhD students)
• Applicant's essay response (350-word limit) to: Describe the desired impact your research will make on the field and society, and why this is important to you. Include any personal, educational and/or professional experiences that have motivated your research interests.
• Applicant's essay response (350-word limit) to: What are your long-term goals for your pathway in computing research, and how would receiving the Google PhD Fellowship help you progress toward those goals in the short-term?

How do I apply for the PhD Fellowship Program? Who should submit the applications? Can students apply directly for a Fellowship?

Check the eligibility and application requirements in your region before applying. Submission forms are available on this page when the application period begins.

India, Latin America and Southeast Asia: students may apply directly during the application period.

Africa, Australia, Canada, East Asia, Europe, New Zealand, and the United States : students cannot apply directly to the program; they must be nominated by an eligible university during the application period.

How many students may each university nominate?

India, Latin America and Southeast Asia : applications are open directly to students with no limit to the number of students that can apply from a university.

Australia and New Zealand : universities may nominate up to two eligible students.

Canada and the United States : Universities may nominate up to four eligible students. We encourage nominating students with diverse backgrounds especially those from historically marginalized groups in the field of computing. If more than two students are nominated then we strongly encourage additional nominees who self-identify as a woman, Black / African descent, Hispanic / Latino / Latinx, Indigenous, and/or a person with a disability.

Africa, East Asia and Europe : Universities may nominate up to three eligible students. We encourage nominating students with diverse backgrounds especially those from historically marginalized groups in the field of computing. If more than two students are nominated then we strongly encourage the additional nominee who self-identifies as a woman.

*Applications are evaluated on merit. Please see FAQ for details on how applications are evaluated.

How are applications evaluated?

In Canada and the United State, East Asia and Latin America, essay responses are evaluated in addition to application materials to determine an overall recommendation.

A nominee's status as a member of a historically marginalized group is not considered in the selection of award recipients.

Research should align with Google AI Principles .

Incomplete proposals will not be considered.

How are Google PhD Fellowships given?

Any monetary awards will be paid directly to the Fellow's university for distribution. No overhead should be assessed against them.

What are the intellectual property implications of a Google PhD Fellowship?

Fellowship recipients are not subject to intellectual property restrictions unless they complete an internship at Google. If that is the case, they are subject to the same intellectual property restrictions as any other Google intern.

Will the Fellowship recipients become employees of Google?

No, Fellowship recipients do not become employees of Google due to receiving the award. The award does not preclude future eligibility for internships or employment opportunities at Google, nor does it increase the chances of obtaining them. If they are interested in working at Google, they are welcome to apply for jobs and go through the same hiring process as any other person.

Can Fellowship recipients also be considered for other Google scholarships?

Yes, Fellowship recipients are eligible for these scholarships .

After award notification, when do the Google PhD Fellowships begin?

After Google PhD Fellowship recipients are notified, the Fellowship is effective starting the following school year.

What is the program application time period?

Applications for the 2024 program will open in March 2024 and close in May 2024 for all regions. Refer to the main Google PhD Fellowship Program page for each region’s application details.

A global awards announcement will be made in September on the Google Research Blog publicly announcing all award recipients.

How can I ask additional questions?

Due to the volume of emails we receive, we may not be able to respond to questions where the answer is available on the website. If your question has not been answered by a FAQ, email:

Africa: [email protected]

Australia and New Zealand: [email protected]

Canada and the United States: [email protected]

East Asia: [email protected]

Europe: [email protected]

India: [email protected]

Latin America: [email protected]

Southeast Asia: [email protected]

See past PhD Fellowship recipients.

Discover our collection of tools and resources

Browse our library of open source projects, public datasets, APIs and more to find the tools you need to tackle your next challenge or fuel your next breakthrough.

• Our Promise
• Our Achievements
• Our Mission
• Proposal Writing
• System Development
• Paper Writing
• Paper Publish
• Synopsis Writing
• Thesis Writing
• Assignments
• Survey Paper
• Conference Paper
• Journal Paper
• Empirical Paper
• Journal Support
• PhD Research Proposal on Machine Learning

We design a PhD research proposal in machine learning (ML) which involves finding a particular issue in a field of study, preparing research queries, proposing techniques and summarizing the possible specialty of the research. Our work includes the following a well-defined problem statement, flawless languages, on time delivery, unlimited revisions and technical discussions. The perfect structure of PhD research proposal is offered at phdservices.org .The following is an overview that helps us to construct a PhD research proposal in ML.

Title of the Proposal

       Enhancing the Robustness of Deep Neural Networks (DNNs) against Adversarial Attacks

Introduction

• We shortly introduce the area of ML and its importance.
• Describing the particular field within ML that we study and solve it.
• To represent the limitations and similarity of harmful threats on DNNs.

Problem Statement

• By examining the issue we challenge the research.
• We offer context and inspiration for why this problem is difficult to overcome.

Research Questions and Assumption

• What are the most efficient ideas to predict and reduce adversarial attacks on DNN?
• Can we build a generalizable model that improves the robustness of these frameworks across various fields?
• How do harmful defenses affect the understandability and basic performance of DNNs?

Literature Survey

• We outline the traditional research on adversarial ML.
• By detecting the gaps we improve the recent techniques and skills.
• To develop framework efficiency we justify the requirement of our research.
• We list down the primary and secondary goals of our research.
• Explaining what our research focuses to attain in terms of theoretical and experimental contributions.

Methodology

• For directing the research we describe the proposed methods.
• We explain data collection, framework development, practical design and validation metrics.
• Discussing our option of ML approaches, models and techniques.

Expected Results

• We detect the possible outcomes and their suggestions.
• To define how our research commits to the area of ML.
• Explaining the possible applications of our powerful neural networks.

Work Plan and Timeline

• Sketch the stages of our research.
• We offer duration for every section involving literature review, data gathering, experimentation and writing.

Bibliography

• By adding a preliminary list of references we scratch the research.

       We recognize that the significance and depth of our proposal is based on the needs of the PhD program that we’re applying and the expectations of prospective mentors. It should also return us the realistic scope of task that is finished within the usual duration for a PhD (actually 3-5 years).

Before submitting our proposal we ensure to:

• Check the Alignment: Make sure that our research interests coincide with our skills and the resources are available at the university.
• Proofread: To find any fault and not clear phases, we should get feedback on our proposal from our experts and professors.
• Concise: Our proposal should be clear and accurate as potential and recognize that it is an outline and not a full paper.
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Get your dissertation proposals also done with top notched quality free from error.   We cater the best needs for your machine learning proposals that meet the standards to your university. Our prices are affordable. Money back policy and on time delivery is our key ethics.

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Machine Learning Research Proposals Ideas

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Our commitment towards working will help you to attain high grade with affordable pricing for all ML research proposals. Our proposals include the summary of the proposed research work and its conclusions. So, it gets easily accepted as here we contribute more to machine learning field. Here our writers have experienced knowledge on machine learning topics which paves the path for successful research.

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• Sentiment Analysis of Audio Files Using Machine Learning and Textual Classification of Audio Data
• MALADY: A Machine Learning-Based Autonomous Decision-Making System for Sensor Networks
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• Hyperparameters Search Methods for Machine Learning Linear Workflows
• High Throughput Ultrasonic Multi-implant Readout Using a Machine-Learning Assisted CDMA Receiver
• Web Content Based Features for Malicious Web Page Detection Using Machine Learning

MILESTONE 1: Research Proposal

Finalize journal (indexing).

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The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

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To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

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Optimizing Machine Learning Model Selection for Perovskite Solar Cells Datasets

Written by Eugene R.

Introduction

In recent years, the energy industry has witnessed a significant shift on the global platform. With climate change affecting different geographic territories, this shift has been driven by the urgent need to address the adverse effects of climate change. To achieve sustainable energy development goals, several countries are now changing their global energy landscape and finding ways to utilize natural resources such as solar energy (Djellouli et al., 2022). This need to reduce dependency on fossil fuels and greenhouse gas emissions comes with multiple opportunities and challenges. For example, solar energy is considered one of the most efficient renewable energy resources. However, to achieve its complete potential, it is important to overcome challenges associated with efficiency, reliability, and integration of solar systems (Zhao et al., 2022).

With the advancement in technology, machine learning (ML) has emerged as a powerful tool that can be used to solve challenges associated with the implementation of solar energy systems. ML is known for its ability to evaluate large datasets, identify patterns, and make accurate predictions (Jha et al., 2017). Its use may suggest unique strategies that can enhance the efficiency of solar energy systems. Similarly, perovskite solar cells have emerged as a promising technology in the field of photovoltaics. These cells are not only flexible but also known for their potential to offer higher efficiency at lower manufacturing costs. Several studies have identified perovskite solar cells as a potential game-changer in the solar industry (Shiogai et al., 2020; Tao et al., 2021). However, the development and optimization of perovskite solar cells remains challenging due to the complexities involved in their performance, material composition, fabrication processes, and device architecture. Salah et al. (2023) noted that optimizing perovskite solar cells can be a time-consuming and resource-intensive process. We propose that ML can be a powerful tool in accelerating the optimization of perovskite solar cells.

Research Aim and Objectives

The role of different ML models in enhancing the efficiency of perovskite solar cell datasets has been studied by both academic and industry experts (Shiogai et al., 2020; Tao et al., 2021). However, there is a lack of comprehensive studies that provide an in-depth comparison of the performance of different ML algorithms across various perovskite datasets. As a result, this research aims to address this gap by systematically evaluating and comparing different ML models used for predicting perovskite solar cell performance. The research also focuses on understanding the difficulties encountered in integrating ML models in perovskite solar cells.

The research has the following objectives:

• To compile and process different perovskite solar cell datasets from literature and experimental sources.
• To implement and evaluate various ML models, such as regressions, decision trees, random forests, support vector machines (SVM), and artificial neural networks.
• To identify the challenges associated with integrating ML models with perovskite solar cells.
• To develop a framework for selecting the most appropriate ML model based on dataset characteristics and prediction tasks.

By leveraging important ML techniques, this research can contribute to the existing pool of knowledge. The outcomes of this study can further help renewable energy experts in making more efficient and data-driven decisions for the global adoption of solar energy systems.

Literature Review

Recently, there has been a significant rise in the generation and use of solar energy all over the world. Enhancing solar energy infrastructure can have multiple advantages for countries, as well as the environment. The application of ML in the solar energy sector has gained considerable attention in recent years. Gandhi et al. (2023) noted that several countries are now looking for optimized approaches to generate solar energy through intelligent techniques. As forecasting the future need of solar energy is one of the first steps in developing a solar energy system, experts are experimenting with technology to meet their requirements. With ML being one of the proven technologies used for data analysis and prediction, it can play a crucial role in the solar energy transformation (Subramanian et al., 2023). The study noted that ML can be used for continuous monitoring of solar cells, modules, and panels. With a combination of deep learning and big data analytics, data can be collected and interpreted to make accurate predictions. Vélez et al. (2024) demonstrated that ML algorithms can be used to quantify linear correlation between synthesis descriptors of perovskite solar cells.

Perovskite materials are widely preferred in multiple scientific fields for their composition diversity. These materials are easily available and possess multiple beneficial properties for the solar application (Shiogai et al., 2020). Li et al. (2019) explored the role of ML in optimizing material composition, developing design strategies, and predicting the performance of perovskite solar cells. Based on 333 data points, new perovskite compositions were synthesized to test the predictability of the model. The study observed that the ML model can be used to predict the underlying phenomena and the performance of perovskite solar cells. In another study conducted by Eibeck et al. (2021), it was observed that baseline ML models perform better on the computational dataset as compared to neural-based models. The study recommended the need for further research to enhance the ability of ML models to predict power conversion efficiency of perovskite cells in well-controlled conditions.

Furthermore, the use of ML in using perovskite material for developing solar cells was studied by Tao et al. (2021). While the study highlighted the potential of using ML in perovskite material for the development of solar cells, it also indicated the need for further research. On the other hand, Salah et al. (2023) provided a comprehensive analysis of the application of ML models for studying complex datasets in the field of solar cell power conversion efficiency. The study indicated the need for further analysis to identify appropriate model selection based on different requirements and selection criteria. Similarly, Li et al. (2021) noted that ML methods have shown remarkable achievements in predicting the basic performance of perovskite materials. However, the unique structural diversity and compositional flexibility of perovskites make it challenging to construct a comprehensive model. This limits the prediction accuracy of ML, and the model may miss out on some fundamental physical information.

Apart from studying the composition and performance prediction of perovskite cells, Zhang et al. (2023) examined the process optimization of perovskite cells using ML. The study used a combination of genetic algorithms and neural networks to optimize the spin-coating parameters for perovskite film deposition. This resulted in a 15% improvement in the film quality of perovskite cells. In another study, Chen et al. (2023) noted that the development of perovskite solar cells is a complex and slow process. To get an ideal cell, manufacturers need to experiment with multiple artisanal samples. This process can be improved by automating the number of consecutive steps in the research process. The study also identified lead halide perovskites as a favourable material for solar cell applications. However, Asghar et al. (2017) argued about the toxicity of lead that can lead to long-term operational instability of solar cells. As a result, it is important to find material that can be advantageous for perovskite solar applications. With appropriate ML models, large datasets demonstrating the use of different materials used in perovskite cells can be analyzed, and a suitable material can be found based on other conditions.

Although these studies demonstrate a successful implementation of ML models in perovskite solar cells, several gaps have been identified in the existing literature. For example, the above studies focus on a single ML algorithm and have limited data for comparing different ML models. This makes it difficult to evaluate the comprehensive application of ML on various models and datasets. Similarly, there is limited information on the interpretability of ML models in connection to perovskite solar cells. Detailed research is essential to facilitate the adoption of ML in the development of perovskite solar cells. Additionally, the impact of different dataset features such as size, diversity, and noise levels needs to be systematically studied to understand model performance for perovskite solar cell applications. This research aims to address these gaps by providing a comprehensive framework for selecting and interpreting ML models for perovskite solar cell datasets.

Research Methodology

Research methods in ML play a crucial role, as the accuracy and reliability of the results are influenced by the research methods used. The nature of the study indicates that the majority of the data used for this research will be quantitative. Thus, a standardized approach will be used for an experimental research design. This includes data collection and preprocessing, model selection, model testing, and model evaluation.

Data collection and preprocessing

To get a better understanding of perovskite solar cell performance, data will be collected from academic journals, books, and online articles. This will provide diversity in dataset characteristics and also give an insight into the different features of perovskite solar cells, such as material composition, fabrication methods, and device architectures. Furthermore, the collected data will be cleaned to handle missing values and avoid any inconsistencies. Preprocessing allows for the identification of key features of datasets and ensure in proper labelling of data (Kamiri & Mariga, 2021). This will further normalize data features to ensure comparability across different datasets. Data analysis is another important aspect of studying the use of ML in perovskite solar cells. It will involve analyzing data to identify patterns, trends, and relationships between earlier studies and their application (Sharifani & Amini 2023).

Model selection

The optimal use of model evaluation, model selection, and algorithm selection plays an important role in developing an appropriate ML model for optimizing perovskite cell datasets (Raschka, 2018). Common methods for model selection include linear regression, decision trees, random forests, support vector machines, gradient boosting machines, and artificial neural networks. After the model section, k-fold cross-validation will be employed to ensure robust performance estimation. In addition to this, ML models will be evaluated using different metrics, such as mean absolute error (MAE), root mean square error (RMSE), R², and domain-specific metrics like perovskite charging efficiency prediction accuracy. The impact of different dataset characteristics will be further analyzed based on model performance.

Model testing and evaluation

A comprehensive analysis of model performance across different datasets will be conducted. This will help in identifying patterns and trends in model performance related to dataset characteristics. Furthermore, a flowchart will be developed to guide model selection based on dataset properties and prediction tasks. The developed framework will later be tested on different perovskite solar cell datasets. The framework will be further refined based on the validation results and expert feedback (Kamiri & Mariga, 2021). Various tools, including programming languages such as Python and R, ML libraries such as TensorFlow and Keras, and statistical packages will be used for accurate model evaluation and selection.

This research proposal provides a comprehensive plan to address challenges faced by researchers in selecting appropriate ML models for perovskite solar cell datasets. This research will systematically evaluate various ML algorithms across diverse datasets. Based on the collected data, a robust framework for model selection and interpretation will be developed. The research aims to offer a comprehensive comparison of different ML models across perovskite solar cell datasets. It will provide an insight into the strengths and limitations of different ML algorithms used for optimizing perovskite solar cell datasets. The research will also enhance the understanding of key factors influencing the performance of perovskite solar cell. By analyzing multiple ML models and datasets, it will facilitate an efficient use of ML in perovskite research. Apart from filling the gaps in the existing literature, this research has the potential to significantly advance the application of ML in perovskite solar cell research. The outcomes of this study can further help in establishing best practices for applying ML in material science, especially in complex systems like perovskite solar cells.

Asghar, M. I., Zhang, J., Wang, H., & Lund, P. D. (2017). Device stability of perovskite solar cells–A review.  Renewable and Sustainable Energy Reviews ,  77 , 131-146 https://doi.org/10.1016/j.rser.2017.04.003

Chen, C., Maqsood, A., & Jacobsson, T. J. (2023). The role of machine learning in perovskite solar cell research.  Journal of Alloys and Compounds ,  960 , 170824 https://doi.org/10.1016/j.jallcom.2023.170824

Djellouli, N., Abdelli, L., Elheddad, M., Ahmed, R., & Mahmood, H. (2022). The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries.  Renewable Energy ,  183 , 676-686 https://doi.org/10.1016/j.renene.2021.10.066

Eibeck, A., Nurkowski, D., Menon, A., Bai, J., Wu, J., Zhou, L., & Kraft, M. (2021). Predicting power conversion efficiency of organic photovoltaics: models and data analysis.  ACS omega ,  6 (37), 23764-23775 https://doi.org/10.1021%2Facsomega.1c02156

Gandhi, R. R., Kathirvel, C., Kumar, R. M., & Ramkumar, M. S. (2023). Solar energy forecasting architecture using deep learning models. In  Machine Learning and the Internet of Things in Solar Power Generation  (pp. 105-121). CRC Press.

Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of Artificial Intelligence.  Renewable and Sustainable Energy Reviews ,  77 , 297-317 https://doi.org/10.1016/j.rser.2017.04.018

Kamiri, J., & Mariga, G. (2021). Research methods in machine learning: A content analysis.  International Journal of Computer and Information Technology (2279-0764) ,  10 (2) https://doi.org/10.24203/ijcit.v10i2.79

Li, C., Hao, H., Xu, B., Shen, Z., Zhou, E., Jiang, D., & Liu, H. (2021). Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning.  Computational Materials Science ,  198 , 11071 https://doi.org/10.1016/j.commatsci.2021.110714

Liu, Y., Yan, W., Han, S., Zhu, H., Tu, Y., Guan, L., & Tan, X. (2022). How machine learning predicts and explains the performance of perovskite solar cells.  Solar RRL ,  6 (6), 2101100 https://doi.org/10.1002/solr.202101100

Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning.  arXiv preprint arXiv:1811.12808 https://doi.org/10.48550/arXiv.1811.12808

Salah, M. M., Ismail, Z., & Abdellatif, S. (2023). Selecting an appropriate machine-learning model for perovskite solar cell datasets.  Materials for Renewable and Sustainable Energy ,  12 (3), 187-198 https://doi.org/10.1007/s40243-023-00239-2

Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications.  World Information Technology and Engineering Journal ,  10 (07), 3897-3904 https://ssrn.com/abstract=4458723

Shiogai, J., Chida, T., Hashimoto, K., Fujiwara, K., Sasaki, T., & Tsukazaki, A. (2020). Signature of band inversion in the perovskite thin-film alloys BaS n 1–x P bx O 3.  Physical Review B ,  101 (12), 125125 https://doi.org/10.1103/PhysRevB.101.125125

Subramanian, E., Karthik, M. M., Krishna, G. P., Prasath, D. V., & Kumar, V. S. (2023). Solar power prediction using Machine learning.  arXiv preprint https://doi.org/10.48550/arXiv.2303.07875

Tao, Q., Xu, P., Li, M., & Lu, W. (2021). Machine learning for perovskite materials design and discovery.  Npj computational materials ,  7 (1), 23 https://doi.org/10.1038/s41524-021-00495-8

Vélez, J., Botero L, M. A., & Sepulveda, A. (2024). Measurement of information content of Perovskite solar cell’s synthesis descriptors related to performance parameters. Emergent Materials, 1-8. Emergent Materials https://doi.org/10.1007/s42247-024-00667-4

Zhang, J., Liu, B., Liu, Z., Wu, J., Arnold, S., Shi, H., & Brabec, C. J. (2023). Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells.  Advanced Energy Materials ,  13 (48), 2302594 http://dx.doi.org/10.1002/aenm.202302594

Zhao, J., Dong, K., Dong, X., & Shahbaz, M. (2022). How renewable energy alleviate energy poverty? A global analysis.  Renewable Energy ,  186 , 299-311 https://doi.org/10.1016/j.renene.2022.01.005

We have 15 Machine Learning (fully-funded) PhD Projects, Programmes & Scholarships

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Machine Learning (fully-funded) PhD Projects, Programmes & Scholarships

Fully-funded phd studentship in automated beta-emitting radioisotope identification and monitoring in boreholes, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (UK Students Only)

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Fully-Funded Research Study @ The Hong Kong University of Science and Technology (Guangzhou) | Unified HKUST, Complementary Campuses

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Hong Kong PhD Programme

A Hong Kong PhD usually takes 3-4 years; the exact length may depend on whether or not a student holds a Masters degree. Longer programmes begin with a probation period involving taught classes and assessments. Eventually all students produce an original thesis and submit it for examination in an oral ‘viva voce’ format. Most programmes are delivered in English, but some universities also teach in Mandarin Chinese.

UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial Intelligence (SUSTAIN)

Ukri centre for doctoral training.

UKRI Centres for Doctoral Training conduct research and training in priority topics related to Artificial Intelligence. They are funded by the UK Government through UK Research and Innovation. Students may receive additional training and development opportunities as part of their programme.

School of Computer Science PhD Studentships

Phd research programme.

PhD Research Programmes present a range of research opportunities shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

Machine learning for extreme scale computational imaging in radio astronomy

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Foundations of deep learning for scalable computational imaging

Deep learning for black hole tomography, computational imaging in low resource settings, sustai cdt - the urki ai centre doctoral training in ai for sustainability is now recruiting for september 2024, real-time subsampled analysis and recovery for high-resolution 3d tomography, fast algorithms for huge dynamic graphs, funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

Fully funded PhD position - Programming Group, Univ. of St.Gallen, Switzerland

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Filtering Results

• Corpus ID: 209436745

PHD PROPOSAL IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• Published 2019
• Computer Science

25 References

Large-scale machine learning and applications, a survey on transfer learning, distributed gaussian processes, deterministic execution on gpu architectures, exact gaussian processes on a million data points, a loewner-based approach for the approximation of engagement-related neurophysiological features, the worst-case execution-time problem—overview of methods and survey of tools, electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network, pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification, supplementary for: deep learning with convolutional neural networks for eeg decoding and visualization, related papers.

Showing 1 through 3 of 0 Related Papers

• Development proposals

Machine Learning Research Proposal

Used 4,872 times

Win machine learning projects when using this ML research proposal template to demonstrate your expertise and skills.

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Created by:

​ [Sender.FirstName] [Sender.LastName] ​

​ [Sender.Company] ​

​ [Sender.StreetAddress] ​

​ [Sender.City] [Sender.State] [Sender.PostalCode] ​

Prepared for:

​ [Client.FirstName] [Client.LastName]

​ [Client.Company] ​

This machine learning research proposal contains all the information regarding the use and application of [Sender.Company] artificial intelligence, machine learning, neural networks, and cognitive science on behalf of [Client.Company] .

Cover Letter

Dear [Client.LastName] ,

Following our earlier discussion, I’d like to present our machine learning research proposal. [Sender.Company] aims to drive innovation and excellence in machine learning research. Our team consists of dedicated researchers with a passion for leveraging machine learning to address real-world challenges. I believe this proposal aligns with the goals and objectives of your organization.

In this proposal, we’ll aim to investigate (Add the specific area/problem) by employing cutting-edge machine learning techniques. The significance of this research lies in its potential to (Highlight the key outcomes).

Given the increasing importance of machine learning in addressing complex issues, this project can potentially make a meaningful impact on (Add the relevant industry). By leveraging (Add specific methodology and algorithms), our team intends to contribute valuable insights to the existing body of knowledge.

In the remainder of this proposal, we’ll share our vision of this machine learning research project and how our methodologies will solve your current problem. We have pride in the quality of our work and strong ethics. We look forward to working with you on this next machine learning research project.

Why Choose [Sender.Company] ​

Choosing [Sender.Company] for collaboration on this machine learning research project is a decision that aligns with your need for (Highlight outcomes). Our goals align seamlessly with the values that drive a successful research endeavor.

Key Advantages

Aligned goals.

​ [Sender.Company] shares a commitment to (Highlight outcomes), ensuring our collaborative efforts are directed towards achieving mutual success.

Cutting-Edge Resources

Equipped with cutting-edge resources, [Sender.Company] provides an optimal environment for the execution of advanced machine learning research.

Unparalleled Combination

​ [Sender.FirstName] [Sender.LastName] stands out as the ideal partner for this machine learning research project, offering an unparalleled combination of cutting-edge resources, a commitment to innovation, and a supportive environment.

Ethical and Impactful Research

Our collaborative approach nurtures ethical and impactful research, emphasizing the importance of responsible and meaningful contributions to the field.

Investment in Success

Choosing [Sender.Company] is an investment in the project’s success, with the assurance of a collaborative and dynamic research journey.

From Our Customers

​ [Sender.Company] has a proven track record of fostering innovation and pushing the boundaries of knowledge. At [Sender.Company] , we are committed to staying at the forefront of technological advancements. Consider some of the things our customers had to say.

(Add client testimonials)

Our team is well-positioned to lead this endeavor by leveraging our cutting-edge methodologies and collective expertise. Meet the dedicated individuals that will be a part of this machine learning research project.

(Employee.FullName)

(Employee.JobTitle)

(Employee.ContactDetails)

Project Summary

This project aims to address (Add the specific area/problem) using advanced machine learning techniques. Leveraging state-of-the-art methodologies and ethical considerations, our research endeavors to provide innovative solutions with direct applicability in (Add domain or industry).

With a focus on practical impact, the project promises to contribute meaningful insights while pushing the boundaries of current knowledge and fostering interdisciplinary collaboration.

Solutions and Deliverables

​ [Sender.Company] will make use of the following research methodology to produce results for this project:

Acquiring relevant datasets related to (Add specific area/problem).

Ensure data quality through rigorous validation and cleansing processes.

Conducting exploratory data analysis (EDA) to gain insights into the dataset’s characteristics.

Engage in thoughtful feature engineering to enhance model performance.

Evaluate a range of machine learning algorithms suitable for addressing (Add specific area/problem).

Fine-tune model hyperparameters through systematic experimentation.

Divide the data into different training and validation sets.

Utilize appropriate evaluation metrics, considering the nature of the problem.

Address any potential biases found in the data and models.

Emphasize model interpretability to enhance the transparency of outcomes.

Compare the performance of the developed models against baseline models or existing methodologies.

Continuously refine the models based on insights gained during the evaluation.

To make this machine learning research project a success, we recommend the following timeline:

Terms and Pricing

The work for this project has been carefully budgeted based on the requirements of the project and the client’s existing budget. Any changes and/or additions will be discussed beforehand, and any additional costs will be made transparent.

Payments for services will be invoiced every (Enter date) of the month till the project is completed.

Payments will be made by (Enter accepted payment method) within (Enter number) days of receiving the invoice. If a client fails to pay an invoice within five (5) days after the due date, late charges of (Enter late fee) will be applied.

If a Client defaults on any of the payments, the [Sender.Company] may suspend the Project or terminate the contract immediately, with a written notice.

Risk Analysis

​ [Sender.Company] anticipates a (Specify minimal, medium, or high) level of risk with this research project. Potential risks include (Mention any risks such as data bias, etc.). We aim to mitigate these risks through (Describe how risks will be accounted for).

Success Measures

After this machine learning research project, [Client.Company] will have conclusive evidence of the project’s success in the form of (Describe what the completed project will result in). Deliverables for this project will arrive in phases as outlined in the Timeline of this proposal. Any delays will be communicated to the client, and extensions will be requested where needed.

​ [Sender.Company] estimates that this project will be completed by approximately (Insert completion date).

Signoff and Acceptance

IN WITNESS WHEREOF, each of the Parties has reviewed this machine learning research proposal and agreed to the work, terms, and conditions listed herein.

​ [Client.FirstName] [Client.LastName] ​

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PhD Proposal: Quantum algorithms for machine learning and optimization

The theories of machine learning and optimization answer foundational questions in computer science and lead to new algorithms for practical applications. While these topics have been extensively studied in the context of classical computing, their quantum counterparts are far from well-understood. For my thesis, I propose to study quantum time and sample complexities of learning and optimization, focusing on three topics: learning distributions, convex optimization, and spectral analysis.Learning distributions aims to estimate properties or latent variables of unknown distributions, which are fundamental questions in machine learning, statistics, and information theory, given that much of science relies on samples furnished by nature. I propose to have a better understanding about quantum algorithms for learning discrete distributions. In particular, tight bounds on the number of samples to estimate Shannon and Renyi entropies have been established in the classical setting. We gave the first quantum algorithms for estimating entropies with up to quadratic speedup on the number of samples. In the future, I plan to study quantum algorithms for other properties, and also quantum algorithms for learning mixtures of discrete distributions.Convex optimization has been a central topic in the study of theoretical computer science and operations research given the fact that they admit polynomial-time solvers. I propose to understand quantum speedups of convex optimization. In particular, for low-rank semidefinite programmings (SDPs), we gave a quantum algorithm with running time only poly-logarithmic in dimension, an exponential speedup compared to classical algorithms. In the future, I propose to study some applications of our quantum algorithm for solving SDPs, and I also plan to find quantum algorithms for solving general convex optimization problems.Spectral analysis aims at understanding the spectrum of data. In particular, principal component analysis finds the subspace with the largest variance in data, a ubiquitous task in unsupervised learning, feature generation, and data visualization. Inspired by the power method, we gave a quantum algorithm that finds the leading eigenvector of a sparse matrix with exponential speedup in dimension compared to classical algorithms. We also proved that the exponential speedup still holds if the data are given online. In the future, I propose to improve the time complexity of our algorithms using more advanced optimization techniques such as the stochastic reduced variance gradient descent method (SVRG). I also plan to study quantum algorithms for high-order spectral analysis, i.e., efficient quantum algorithms for tensor decomposition.

Examining Committee:

Chair: Dr. Andrew Childs Dept. rep: Dr. Furong Huang Members: Dr. Aravind Srinivasan Dr. Xiaodi Wu