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background in inorganic or materials chemistry or materials physics. Experience with extraction and separation of materials. Experience in critical mineral extraction and characterization methods. A strong
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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Requirements: The prospective candidate should be well-versed with deep neural networks, have experience working on PyTorch or similar DL frameworks, programming in Python (preferred), NLP packages and pipelines
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relationships via digital manufacturing practices Basic experience with AI/ML techniques Publish research in peer reviewed journals and conferences Support R&D staff members on their projects General support of
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characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-approaches that allow integration of different theory, simulation, and experimental protocols. The research is designed to provide opportunities for development of your experience and scientific vision