Research proposals and academic grants

POSTED ON Jul 07, 2025

North Carolina Space Grant has announced its 2025-2026 Undergraduate Research Scholars. These scholars will conduct research in various fields, including astrophysics, physics, medical laboratory science, bioengineering, aerospace engineering, and biology.

The awarded students will conduct their research at industrial sites, government facilities, and on their home campuses at the University of North Carolina at Chapel Hill, the University of North Carolina at Asheville, Winston-Salem State University, North Carolina Agricultural and Technical State University, and NC State University.

Throughout their STEM research journeys, they will develop relationships with university mentors and NASA experts, a benefit emphasized by previous scholars.

The 2025-2026 NC Space Grant Undergraduate Research Scholars will engage in projects focusing on a range of topics, including the identification and validation of young exoplanets, exploring the nature of dark matter using the James Webb Space Telescope, emulsion plate analysis, lunar regolith studies, radio data analysis in the Skynet Robotic Telescope Network, aeroelastic wings, nuclear thermal propulsion reactor cores, and astronaut health.

Nvidia has announced that its Academic Grant Program is now accepting research proposals in three key areas: Generative AI Training and Model Development, Generative AI Alignment and Inference, and Robotics and Edge AI.

Proposals must be submitted to the Academic Grant Program by September 30.

The call for proposals for each interest area includes eligibility information and compute resources available for request, such as DGX Spark, RTX PRO 6000 Blackwell, Jetson Orin Nano, Jetson AGX Orin, and A100 GPU hours.

It also provides submission instructions and a required proposal template. Award decisions are expected to be made in December.

The areas of interest are as follows:

Generative AI Training and Model Development: This focus encompasses foundation models for scientific domains, as well as techniques for training, scaling, and tailoring models to specific needs. Specific techniques of interest include distillation, multimodality, and modularity.

Generative AI Alignment and Inference: This area emphasizes techniques for customizing, enhancing, and operating models. Essential topics include explainability, reliability, and inference efficiency, as well as multi-model systems and retrieval-augmented generation.

Robotics and Edge AI: Key topics in this area involve robotics, autonomous vehicles, 5G/6G technologies, intelligent environments, and federated learning.