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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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multiple types of sensing modalities, where this expertise is applied to solve critical national problems in energy and security. Demonstrated knowledge of emerging AI and machine learning techniques as
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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Write software to support cutting edge research and facilitate Proof of Concept demonstrations Implement, evaluate, and enhance machine learning pipelines for cybersecurity applications Basic
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hardware. As part of our team, you will perform research to develop new scalable quantum simulation algorithms, based on multi-linear representation theory, and apply them to real world applications spanning
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programming. Incorporate feedback from compiler/runtime systems, including MLIR-based ecosystems (Mojo, Julia, Rust). Compiler Infrastructure & LLVM/MLIR Integration: Extend LLVM/MLIR pipelines to support AI
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team of scientists to develop in situ monitoring technologies for DED-based AM processes. Design, integrate, and deploy multi modal sensor systems (including but not limited to optical, thermal, acoustic
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: Development of new protocols to control coherent spin dynamics for optically accessible spin defects. Enable new spin-based quantum sensing protocols in low temperature and high field environments. Maintain and
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of