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multi-physics simulations on high performance computing (HPC) and ML Experience working in a multi-disciplinary research environment Special Requirements: Applicants cannot have received their Ph.D
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processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply analytical and modeling approaches to interpret experimental results. Collaborate with internal and external research
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avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning. Develop high accuracy point cloud registration and mapping workflows. Plan and conduct experiments
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industrial heat pumps). The role also involves research and analysis of technologies and practices for increasing the energy efficiency of the industrial sectors. The primary objective is to deliver the direct
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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setups; plan and execute experimental campaigns; perform thermofluid analyses; and analyze and interpret results. Conduct experimental and laboratory research on thermal energy storage technologies
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motivated and talented postdoctoral research associate(s) to join our dynamic team. This role lies at the exciting intersection of High-Performance Computing (HPC) and Artificial Intelligence (AI) at scale
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data
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structure-chemistry-property discovery in magnetic solids and validate them against multi-modal experimental measurements Perform high-throughput first-principles electronic structure calculations (e.g. DFT
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to contribute to development of alloys with desirable advances in mechanical properties, thermal/electrical properties, and processability. A background in solidification processing, high pressure die casting