AF STFP Neuromorphic Computing
The Air Force seeks innovative neuromorphic computing hardware and algorithms for low-power, edge-based machine learning in size and weight-constrained environments.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025 (Application Deadline)
Funding Amounts: Base stipend approximately $95,000 plus $5,000 travel allowance; stipend increases with experience; typical award duration 2-3 years.
Summary: Supports postdoctoral and senior researchers developing innovative neuromorphic computing hardware and algorithms for low-power, edge-based machine learning in size, weight, and power (SWaP) constrained environments.
Key Information: Open to U.S. citizens; requires contacting a Research Adviser prior to application; relocation and health insurance benefits included.
Description
This fellowship opportunity, offered through the Air Force Science and Technology Fellowship Program (AF STFP) under the NRC Research Associateship Programs, focuses on advancing neuromorphic computing technologies. Neuromorphic computing aims to emulate biological brain functions such as trainable networks of neurons and synapses using non-traditional, highly parallelizable, and reconfigurable hardware. This approach is particularly promising for low-power, edge-based machine learning (ML) applications that operate without reliance on cloud computing, addressing the limitations of size, weight, and power (SWaP) in deployed systems.
The research scope includes mathematical modeling, hardware characterization and emulation, hybrid CMOS architecture design, and algorithm development for neuromorphic processors. Emphasis is placed on leveraging the physical properties of devices to perform computation, with interest in technologies such as optics/photonics, memristors/ReRAM, metamaterials, nanowires, and superconductors. The goal is to develop imaginative and efficient solutions to meet future Air Force and Space Force needs for non-cloud-tethered ML on SWaP-limited assets.
