Postdoctoral Position: Neuromorphic and In-Memory Spintronic Systems for Secure AI Hardware

This postdoctoral position focuses on advanced research in neuromorphic computing and in-memory spintronic systems tailored for secure artificial intelligence hardware. Candidates should possess deep expertise in neuromorphic system design, including knowledge of brain-inspired computing architectures and their implementation. A strong background in spintronics is essential, particularly in leveraging spintronic devices for in-memory computing to enhance performance and energy efficiency. Experience with secure AI hardware implies familiarity with hardware-level security mechanisms, cryptographic techniques, and methods to protect AI models against attacks. The role likely requires proficiency in experimental and theoretical methods related to spintronic materials and devices, as well as circuit and system-level integration. Candidates should be adept at interdisciplinary research combining physics, materials science, electrical engineering, and computer science. Skills in device fabrication, characterization, and simulation tools relevant to spintronic and neuromorphic systems are advantageous. The position demands a high level of academic training, typically a PhD, with substantial research experience in related fields. Strong analytical, problem-solving, and communication skills are important for collaboration and dissemination of findings. Overall, the candidate should be capable of advancing secure AI hardware through innovative neuromorphic and spintronic technologies, contributing to both fundamental understanding and practical implementations.