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into closed-loop control frameworks Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds Apply machine learning techniques to predict thermal system
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optimization across quantum and classical computing resources. Conduct hardware-software and application-system co-design by considering interactions among quantum hardware characteristics, HPC architectures
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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into emerging paradigms
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, including hardware, software, tools, and applications Apply your proficiency in developing sophisticated software systems, such as operating systems, compilers, adaptive runtime systems, or scalable
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physics, particle physics, or a closely related field completed within the last 5 years Preferred Qualifications: Hardware and/or data analysis/simulations experience relevant to rare event searches
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research within past five years. Preferred Qualifications: Ability to work independently to design and deploy methods at scale. Familiarity with hardware-software co-design, memory hierarchies (DDR, HBM