71 biosignal-processing-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
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. in Quantum computing, Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a closely related discipline, with demonstrated knowledge of or research experience in
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within a multi-disciplinary research environment consisting of computational scientists, computer scientists, electrical engineers, domain scientists, and applied mathematicians conducting basic and
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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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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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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computing for science and engineering; focuses on grand-challenge science and engineering applications; procures largest-scale computer systems (beyond typical vendor design points) and develops high-end
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applying machine learning for materials and/or process discovery, particularly quantum and/or microelectronic materials Expertise in using or developing agentic tools for automation of scientific discovery