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NVIDIA Opens 2027–2028 Graduate Fellowship Applications with $60K Awards
NVIDIA's 26th annual Graduate Fellowship Program now accepts applications for the 2027–2028 academic year, offering up to $60,000 per doctoral student plus a mandatory research internship. The program targets AI, HPC, robotics, and accelerated computing research.

NVIDIA has launched the application window for its 2027–2028 Graduate Fellowship Program, marking the 26th year of an initiative that has distributed roughly $8 million across more than 220 grants since 2002. Doctoral candidates who have completed at least one year of Ph.D. studies may apply for awards of up to $60,000, provided their research aligns with NVIDIA's technology stack — spanning artificial intelligence, machine learning, autonomous systems, computer graphics, robotics, healthcare, and high-performance computing.
Internship Requirement and Timeline
A distinctive feature of the fellowship is a mandatory, in-person internship at an NVIDIA research office during summer 2027, preceding the fellowship year. This hands-on experience connects students directly with the company's engineering teams and accelerated computing infrastructure. The application deadline is October 30, 2026, giving candidates a narrow window to prepare competitive proposals.
Why it matters for GPU / AI infrastructure: Fellowship programs like this seed the next generation of algorithms and workloads that ultimately run on GPU clusters in cloud and on-premises environments. As doctoral researchers push boundaries in model architecture, simulation, and real-time inference, they create demand for more capable, scalable GPU compute — the very capacity AiGpu provisions for enterprises across the Middle East.
For research groups and university labs in the UAE and wider GCC, the fellowship represents a direct funding channel to advance projects that can later be deployed on regional AI infrastructure. Tracking these awarded research directions also helps infrastructure providers anticipate emerging workload patterns and optimize GPU fleet planning accordingly.
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By AiGpu Editorial · Editorial rewrite based on public reporting (NVIDIA Blog)
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