Several PhD Positions - Co-Design for In-hardware Learning, Associative Memories and Kolmogorov-Arnold Networks

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Area of research: . PHD Thesis. Job description: Several PhD Positions - Co-Design for In-hardware Learning, Associative Memories and Kolmogorov-Arnold Networks . We are seeking highly motivated PhD candidates to join our research team and work at the intersection of brain-inspired computing, novel circuits and architectures, and machine learning. The Peter Grünberg Institute - Neuromorphic Compute Nodes (PGI-14). conducts research across several areas with the goal of closing the gap between the computational abilities of modern computers and those of the brain, especially in terms of energy efficiency. We develop novel computing capabilities by incorporating some of the remarkable features found in biological brains. We aim to lower the energy consumption of today’s hardware while expanding our understanding of the fundamentals of computation. Practical application areas include AI and Machine learning, intractable NP-hard problems, scientific computing, and signal processing. Your Job . Several positions are available in distinct project areas:. 1) Associative memory based computing: This project will utilize concepts including Dense Associative Memories and Modern Hopfield Networks to both a) physically realize such systems in electronics, particularly using novel content-addressable memory (CAM) designs, including tape-out opportunities, and b) exploring computing applications