Reliable Learning Algorithms for Resource Constraint Applications

This research opportunity involves basic and applied research in distributed optimization in contested environments, big-data analytics over resource constraint networks, and distributed resource-aware learning. The project aims at developing new frameworks for iterative learning with reduced computational complexity. Candidates are expected to conduct fundamental research in collaboration with ARL scientists and engineers to build a foundation for distributed machine learning. This position is part of the Army Research Laboratory Research Associateship Program (ARL-RAP), designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. The program supports researchers in diverse fields including applied mathematics, atmospheric characterization, signal processing, nanotechnology, material science, and computational and information sciences.