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device-to-architecture level models of emerging nanoscale devices (spintronic, resistive, or hybrid) for in-memory and neuromorphic computing. Exploring hardware-level security mechanisms based
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of neural network architectures (as a plus: PINNs, neural operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing
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