Please see below for an upcoming limited submission for a graduate student fellowship!
Apple Scholars in AI/ML – September 15, 2023: internal deadline September 4
See attachment; we have been asked not to share these guidelines outside of our university. Password is 2024AppleScholars.
- Nominee must be enrolled full time at the nominating university at the start of Fall 2024, and expect to be enrolled through the end of the 2024/2025 academic year
- Nominee should be entering their last 2-3 years of study as of Fall 2024
- Nominee must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2024 to Summer 2026)
Required Materials (see website for more details):
- Student CV and publication list
- Research Abstract (200 word maximum)
- Research statement covering past work and proposed direction for next 2 years (5 page maximum, including citations, in a legible font size) clearly stating the hypothesis and expected contributions to the chosen research area.
- 2 letters of recommendation, one from current advisor (1 page maximum per letter)
- Link to most recent published work (optional)
- Please also prepare to submit the estimated tuition and fees(including enrollment, health insurance, and books) for the nominee’s 2022-2023 academic year (or PhD student salary / bursary stipend, if regionally appropriate).
- Send to firstname.lastname@example.org by Monday, September 4.
- Please submit near-complete versions of required materials #1-5, except for #4 (letters of recommendation): you may submit only the names of expected external references (external letters needed for the solicitation are not required for the internal submission).
- Please also include your primary choice of research area, and optionally a secondary choice, from the following 12 areas (see attachment for more information): Privacy Preserving Machine Learning, Human Centered AI, AI for Ethics and Fairness, AI for Accessibility, AI for Health and Wellness, ML Theory, ML Algorithms and Architectures, Embodied ML, Speech and Natural Language, Computer Vision, Information Retrieval and Knowledge, Data-Centric AI.
- Please indicate whether the nominated student identifies as a member of a traditionally underrepresented group in the technology industry.
Number of nominations per School/Department:
- Schools/Departments should coordinate to submit internally no more than the number of nominations that GT may submit collectively.
- Each university is limited to 3 nominations; it is encouraged that at least one nomination be used for a student who identifies as a member of a traditionally underrepresented group in the technology industry.
- Each university is also limited to one nomination per area from the 12 areas listed, so the submissions to CoC from a given School/Department should represent distinct areas. For submissions that overlap areas, we can consider primary and secondary areas and adjust as needed.