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Field
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detectors, ultra-high vacuum, tritium gas handling, magnetometry, cryogenic engineering, charged-particle trapping, atom trapping and cooling, RF/microwave cavities and radiation detection, FPGAs, Python, C
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architectures Support lab activities related to designing and optimizing AI implementations across various platforms, including embedded and edge computing environments (e.g., Jetson, Raspberry Pi, FPGA
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(e.g., hardware trojans, side-channel exposure). Co-develop testbenches for hardware simulations and chiplet-level threat modelling. Collaborate closely with FPGA and IC prototyping teams to deploy AI
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Stanford University / SLAC National Accelerator Laboratory | Menlo Park, California | United States | about 2 months ago
-the-Standard-Model searches using radioisotopes implanted in superconducting cryogenic sensors Development of ASIC electronics for sensor readout in cryogenic environments Nuclear structure measurements of 0𝜈ββ
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implementation, including FPGA controlling Additional Information Selection process The selection process consists of an initial evaluation of the candidates' CV, motivation letter and specific merits
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. - Hardware acceleration for the development of Artificial Intelligence & Machine Learning techniques using FPGA or other embedded hardware accelerators. - Management, configuration and optimisation
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activities will be developed in the Power Electronics area, focusing on programming control algorithms on a Xilinx FPGA. This project aims to implement internal fault tolerance in a power electronics
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/VHDL/SystemVerilog), logic synthesis, and have exposure to PnR flows (place & route, timing closure, power estimation). You are skilled in modeling and simulation methodologies for exploring PPA
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development proposals and funding bids. The successful candidate will have significant prior experience of: power semiconductor switching converters such as oscilloscopes programming MicroSemi FPGAs in
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project. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power