Apple Scholars in AI/ML

2025 PhD Fellowship Nomination Guidelines

Apple Scholars in AI/ML PhD fellowship program

The Apple Scholars in AI/ML PhD fellowship program recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. The PhD fellowship in AI/ML was created as part of the Apple Scholars program to support the work of outstanding PhD students from around the world, who are pursuing cutting edge research in machine learning and artificial intelligence.

At this time, we are only able to review nominations from universities for students enrolled full time at an invited university. While you may share this document with departments at your university, please do not share these guidelines outside of your university.

Important Dates

A link to the nomination portal will be sent out on Tuesday 19 August 2025.

Nomination window opens: Tuesday 19 August 2025

Nomination window closes: Tuesday 9 September 2025

Decision announced: All nominating universities will hear back regarding the status of their nominations by December 2025.

Apple Scholars in AI/ML PhD fellowship: Award Details

The fellowship award is comprised of a monetary gift and professional mentorship.

All monetary gifts are disbursed to the nominating university annually at the beginning of each academic year, and are conditional upon the Apple Scholar’s full time enrollment with the university for the upcoming year to which the monetary gifts would apply and continuing to meet the eligibility criteria.

The award of the Apple Scholar in AI/ML PhD fellowship does not represent an employer/ employee relationship between Apple and the receiving university and/or student. Apple Scholars may separately be invited to apply for Apple internships; participating in an internship is not a requirement or a guarantee of the fellowship.

Universities receive the following in support of the research of their Apple Scholar(s):

North America
The monetary award will be disbursed annually to the eligible nominating university in the form of an unrestricted gift with the intention to fully support the research of the university’s successful nominee(s). Please confirm with your university development or corporate relations office that they are able to accept the monetary award as an unrestricted gift before nominating students.

• Gift amount covering full tuition and fees (enrollment fees, health insurance, and books) for two (2) academic years
• Up to $40,000 USD gift each year to help with actual living expenses and related expenses
• $5,000 USD gift each year to support research-related travel and associated expenses
• Mentorship with an Apple researcher

Europe, Australia
The monetary award will be disbursed annually to the eligible nominating university in the form of an unrestricted gift with the intention to fully support the research of university’s successful nominee(s).

Please confirm with your university development or corporate relations office that they are willing to accept this gift structure before nominating students.

Award amounts will vary by country.

• Monetary gift each year for two (2) academic years to help with actual living expenses and related expenses
• $5,000 USD gift each year to support research-related travel and associated expenses
• Mentorship with an Apple researcher

Asia-Pacific (Mainland China, Hong Kong, Japan, Korea, Singapore)
The monetary award will be provided in an annual funding structure determined directly with the eligible nominating university, either as a sponsored research agreement or an unrestricted gift, disbursed annually.

• Up to $25,000 USD monetary gift each year for two (2) academic years to help with actual living expenses and related expenses
• $5,000 USD gift each year to support research-related travel and associated expenses
• Mentorship with an Apple researcher

Latin America, Africa (Argentina, Brazil, Chile, Colombia, Mexico, South Africa) The monetary award will be disbursed annually to the eligible nominating university in the form of an unrestricted gift to support the research of the university’s successful nominee(s).

• Up to $20,000 USD monetary gift each year for two (2) academic years to help with actual living expenses, research-related travel and related expenses
• Mentorship with an Apple researcher

Nomination Guidelines

We believe that technology for everyone should be made by everyone, and that research is strengthened by a diversity of perspectives and lived experiences. We aim to create an inclusive and equitable nomination process that amplifies a wide range of backgrounds, experiences and voices in the research community. With that in mind, invited universities may nominate students per the following guidelines.

Nomination Rules
Invited universities may nominate up to three (3) PhD students pursuing research relevant to the research areas listed in the nomination guidelines. This limitation is applied per institution, not per department. Each nominated student must be submitted for a unique research area. We will not accept nominations for multiple students under a single research area from the same university. Applications will be reviewed based solely on the strength of the submitted materials.

Initial and Ongoing Nominee Eligibility
Nominees must meet all of the following criteria to be considered:

• Nominee must be enrolled full time at the nominating university at the start of Fall 2026, and expect to be enrolled through the end of the 2027/2028 academic year;
• Nominee should be entering their last two to three (2-3) years of study as of Fall 2025; and
• Nominee must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2026 to Summer 2028).

Review Criteria
Nominations are reviewed and moved forward by Apple based on the strength of the research proposal, the impact the nominee has had on the field thus far (both as a researcher and community citizen), and their demonstrated potential as a leader and collaborator in the field.

When reviewing the research proposal, we consider the following:

Clearly stated research issue and proposed direction, novelty of the proposal, scientific merit of the proposed approach, potential for impact, and alignment with research areas highlighted by Apple. We also consider, in addition to the aforementioned research acumen, the unique perspective and experience each nominee brings to the field.

Required Materials

Universities should submit the following materials for each student’s nomination:

• Student CV and publication list
• Research Abstract (200 word maximum)

• Research statement covering past work and proposed direction for next 2 years (4 page maximum, including citations, in a legible font size) clearly stating the hypothesis and expected contributions to the chosen research area. Personally identifiable information is redacted for phase one reviewers. We recommend not including personally identifiable information in the main body of the research statement in order to maintain research statement clarity for reviewers of the redacted copy.
• 2 letters of recommendation, one from current advisor (1 page maximum per letter)
• Link to most recent published work (optional)

Documents must be submitted as PDFs with the file naming convention:

• “Last name, First name: Research Statement”
• “Last name, First name: Resume”
• “Last name, First name: Letters of recommendation” (this document should include both letters of recommendation combined into a single PDF)
• Please also prepare to submit the nominee’s estimated tuition and fees (including enrollment, health insurance, and books) for the nominee’s 2025-2026 academic year (or PhD student bursary stipend, if regionally appropriate).
• The amount entered will be used for planning purposes only, and will not impact final Scholar recommendations and/or the actual gift amount, if awarded.

Privacy Policy

• Nomination packet materials should not contain confidential or proprietary research
• Remove or redact birth date, any other personally identifiable information, and/or photographs of the nominee if they appear on submitted materials.
• All information submitted by your institution will only be used by Apple for the purposes of conducting the Apple Scholars in AI/ML PhD Fellowship program, and is intended at all times be handled in accordance with Apple’s Privacy Policy.
  

Research Areas

Nominees should be pursuing research in one or more of the following research areas.

The subtopics listed under each research area are not meant to be exhaustive or prescriptive, but rather highlight areas of particular interest to Apple.

When entering a nomination, the nominator will be asked to select up to two (2) research areas that the nominee feels are most aligned with their work.

There is no single research area that Apple prioritizes over another. We encourage schools and nominees to select the area(s) most relevant to the research.

Privacy Preserving Machine Learning
Privacy Preserving Machine Learning focuses on developing techniques to analyze data without compromising the privacy of individuals.

• Sub topics: Federated Learning, Differential Privacy, Cryptographic Tools, Secure Multiparty Computation

Human Centered AI
Human Centered AI seeks to design, develop, and deploy AI systems that are aligned with human values and needs.

• Sub topics: ML for Multimodal Interaction, Social Signal Processing, ML Design and Human Factors, Usable ML Tools and Products, Interactive ML, Model Personalization, Human-in-the-loop ML

AI for Ethics and Fairness
AI for Ethics and Fairness focusses on developing AI systems that are unbiased and ethical.

• Sub topics: Mitigating Bias, Fairness in AI, Interpretable AI, Introspection, Robustness

AI for Accessibility
AI for Accessibility focuses on developing AI systems to help people with disabilities to interact with the world in new ways.

• Sub topics: Accessible User Experiences, Automatic Personalization/Adaptation, Interactions via New or Combined Modalities, Participatory Design with People with Disabilities

AI for Health and Wellness
AI for Health and Wellness on developing AI systems to improve healthcare outcomes and promote personal wellness.

• Sub topics: Foundation models and LLMs for Health, ML and RL for Mobile Health, Time Series Representation Learning, Physiology-Informed Machine Learning, Modeling MultiModal Sensor Data, Causal Modeling, Human behavior

ML Theory
ML Theory focuses on understanding the mathematical foundations and theoretical properties of machine learning algorithms.

• Sub topics: Understanding ML, Generalization, Optimization, Foundations of Generative Models, Imbalanced Data Theory, Out-of-Distribution setting

ML Algorithms and Architectures
ML Algorithms and Architectures focuses on developing new algorithms, models, and architectures to improve the performance and efficiency of machine learning.

• Sub topics: Foundation Models, Diffusion Models, Hallucinations, Debiasing Data and Models, Auto ML, Model Compression, Architecture / Search, Optimization, Model Representation, Interpretability, Large-Scale ML, Imbalanced Data, Unsupervised and Self Supervised Representation Learning

Interactive ML and Agents
Interactive ML and Agents focuses on developing intelligent agents that can learn to interact with the physical world through trial-and-error learning.

• Sub topics: Inference and Resource Constrained ML, Embodied Foundation Models, Imitation Learning, Multi-Output Models, Reinforcement Learning, Hardware/Software Integration, Hardware Aware ML Training

Speech and Natural Language
Speech and Natural Language focuses on developing algorithms and models to understand or generate spoken or written human language.

• Sub topics: Large Language Models, Conversational and Multi-Modal Interactions, Speech Recognition, Text to Speech, Machine Translation, Language Modeling and Generation

Computer Vision
Computer Vision is a field that focuses on developing algorithms and models to analyze and interpret visual information captured through digital interfaces.

• Sub topics: Semantic Scene Understanding, Video Understanding, 3D Scene Understanding, Efficient Deep Learning for Computer Vision, AI for Content Creation, Continual Learning, Computer Vision for AR/VR, Computer Vision with Synthetic Data, Language and Vision, Computational Photography, Vision for Robotics, Foundation Model for Industrial Machine Vision, Vision for Industrial Robotics

Information Retrieval, Ranking and Knowledge
Information Retrieval, Ranking and Knowledge focuses on developing algorithms and models to extract, organize and infer meaningful information from large amounts of data and serve such information to satisfy user needs.

• Sub topics: Knowledge Extraction, Knowledge Inference & Reasoning, Large-Scale Graph Data Management, Machine Learning and Data Systems Integration, Information Retrieval, Recommendation System

Data-Centric AI
Data-Centric AI focuses on developing machine learning techniques to effectively manage, process, and analyze large volumes of data, while also ensuring data efficacy, efficiency, and fairness.

• Sub topics: Multi Modal Language Models, Data Efficacy, Data Efficiency, Data Generation, Data Fairness, Synthetic Data Generation, Dataset Creation, Data and Annotation, Active Learning, ML-Enabled Data Annotation, Augmentation and Curation, Transfer Learning with Limited Data, Unsupervised and Weakly-supervised Anomaly Detection, Synthetic Defect Generation and Simulation, Sim2real Transfer Learning

FAQ

How will the award be disbursed?
For all schools in North America, the fellowship award is given to the nominating university or associated foundation as an unrestricted gift.

For schools outside of North America, the fellowship award is given to the nominating university (or the US-based foundation that accepts gifts on behalf of the university) as an unrestricted gift.

For schools in mainland China, funds will be distributed to the school as a sponsored research agreement unless otherwise discussed with the school.

Can students submit their own nominations?
No. Students should be chosen and nominated by their institutions, and have a member of the faculty or staff upload their application materials. Students will receive a consent email after their application is submitted, confirming that they agree to be nominated for the fellowship.

What if a student graduates before the two year fellowship is over?
The fellowship award is disbursed on an annual basis at the beginning of each academic year, conditional upon the Apple Scholar's full time enrollment in their program to which the monetary gifts would apply and continuing to meet the eligibility criteria. If a student is not enrolled full time for the second academic year, the award amount may be reduced or omitted from the second year at Apple’s discretion.

Can first year students be nominated for the fellowship?
Students attending universities in the United States who are starting the first year of their PhD at the time of nomination (i.e., starting their PhD in in the 2025 / 2026 academic year) may not be nominated.

First year students (at the time of nomination) in programs outside of the United States may be nominated.

The program is targeted towards mid-to-late career PhD students with an established research and publication record.

Can students from departments other than Computer Science and Computer Engineering be nominated?
Yes. Nominees can be working towards their PhD in computer science or adjacent fields. Nominees from engineering fields, statistics, informatics and related disciplines who are conducting research in one of the categories listed above are welcome.

Schools will still be limited to three (3) nominations per institution, not per department. We recommend schools designate one (1) individual to submit all nominations, to ensure you do not exceed the limit.

Can the student accept other fellowships if they become an Apple Scholar?
The Apple Scholars in AI/ML PhD fellowship is intended to be a fully funded fellowship, so that Apple Scholars would not require any additional funding for the duration of the program. Thus, Apple Scholars may not hold other industry-funded fellowships during their their time as an Apple Scholar. Once a university and its student nominee have committed to the Apple Scholars in AI/ML PhD fellowship in writing, we would ask them to withdraw any remaining nominations they have to other industry-funded fellowships.

Can the nominator list multiple advisors in the application?
Yes, you may list as many academic advisors as the nominee has. A letter of recommendation from any of the listed advisors can fulfill the “letter from current advisor” requirement.

Can the nominator submit more than two letters of recommendation?
No. Please limit letters of recommendation to two. If more than two are submitted, we will remove any letters that appear after the first two before reviewing the packet.

Can the nominator save an unfinished application and finish it later?
No. The application tool does not allow you to save work in progress. Once you click “Submit” you cannot make any changes to your application. We strongly recommend crafting your responses in another document and returning to the application page when you’re ready to input and submit. Upon clicking “Submit”, you will receive an automated “Thank you” email acknowledging your submission.

Can the nominator edit or change the nomination application after I submit?
Applications cannot be edited after you click “Submit”. We strongly recommend crafting your responses in another document and returning to the application page when you’re ready to input and submit.

Upon clicking “Submit”, the nominee will receive an email asking for their consent to be nominated. They must complete this step before the nomination is complete.

Once they have completed this step, the nominator will receive an email confirming the nomination is fully submitted. This final email may be sent up to 3 business days after the nominee consents to their nomination.

If you are concerned that you made an error in your application packet, please email: aiml_scholars@apple.com before the application deadline to determine if edits can be made