NVIDIA offers up to USD 60,000 fellowships to PhD students for model collaboration
NVIDIA has rolled out a new research fellowship program that will award up to $60,000 to PhD candidates working on collaborative machine‑learning systems. The initiative, listed under “Research & Benchmarks,” signals the company’s intent to back projects that move beyond single‑model pipelines. By earmarking substantial funding for scholars at institutions like the University of Washington and Harvard, NVIDIA is nudging the academic community toward experiments where separate models, each trained on distinct datasets, can work together.
The program’s focus on “open, decentralized and collaborative AI” aligns with a broader push to break the silos that often limit current AI deployments. As the fellowships open for applications, the expectation is that recipients will explore ways to let diverse models compose, complement, and exchange insights—an approach that could reshape how future systems are built and shared. This backdrop frames the remarks from two of the program’s early participants.
- Shangbin Feng, University of Washington -- Advancing model collaboration: multiple machine learning models, trained on different data and by different people, collaborate, compose and complement each other for an open, decentralized and collaborative AI future. - Shvetank Prakash, Harvard University -- Advancing hardware architecture and systems design with AI agents built on new algorithms, curated datasets and agent-first infrastructure. - Irene Wang, Georgia Institute of Technology -- Developing a holistic codesign framework that integrates accelerator architecture, network topology and runtime scheduling to enable energy-efficient and sustainable AI training at scale.
NVIDIA's latest Graduate Fellowship round awards up to $60,000 to ten Ph.D. candidates. The program, now in its 25th year, continues to target research aligned with the company's accelerated‑computing focus.
Recipients will first complete a summer internship before beginning a year‑long fellowship, a structure that mirrors previous cohorts. Selections came from a highly competitive applicant pool, though the exact number of submissions was not disclosed. One awardee, Shangbin Feng of the University of Washington, describes his work as advancing model collaboration, where multiple machine‑learning models trained on disparate data sets can compose and complement each other for an open, decentralized AI future.
Another fellow, Shvetank Prakash of Harvard, echoes similar ambitions. The initiative spans all areas of computing innovation, but whether the funding will translate into measurable breakthroughs remains unclear. NVIDIA’s continued investment suggests confidence in academic partnerships, yet the long‑term influence on its product roadmap is not detailed.
As the fellows embark on their internships, the academic community will watch for tangible outcomes.
Further Reading
- NVIDIA Awards up to $60,000 Research Fellowships to PhD Students - NVIDIA Blog
- NVIDIA Awards up to $60,000 Research Fellowships to PhD Students (2026–2027 Recipients) - NVIDIA Blog
- Applications Now Open for $60,000 NVIDIA Graduate Fellowship Awards - NVIDIA Blog
- NVIDIA Graduate Fellowship Program - NVIDIA Research
- NVIDIA Graduate Fellowship Program 2026: $60,000 Funding - MyScholarHQ
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Common Questions Answered
What is the monetary value of the NVIDIA Graduate Fellowship and who is eligible to receive it?
NVIDIA's Graduate Fellowship provides up to $60,000 to each awardee. The funding is targeted specifically at Ph.D. candidates who are conducting research on collaborative machine‑learning systems.
Which universities are mentioned as recipients of the NVIDIA fellowship for model collaboration research?
The article cites scholars from the University of Washington and Harvard University as fellowship recipients. These institutions are highlighted for work on multiple models that train on different data and collaborate.
How does the NVIDIA fellowship program structure the award period for its fellows?
Awardees first complete a summer internship at NVIDIA, followed by a year‑long research fellowship. This two‑stage format mirrors previous cohorts and integrates practical experience with academic research.
What research themes does NVIDIA emphasize for this 25th‑year fellowship round?
NVIDIA focuses on model collaboration, hardware architecture, and AI agents built on new algorithms and curated datasets. The goal is to advance decentralized, collaborative AI systems and agent‑first infrastructure.