71 image-processing-and-machine-learning "UCL" Postdoctoral positions at Oak Ridge National Laboratory
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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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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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-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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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
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, electrical engineering, computer engineering, computer science, aerospace engineering, or a closely related field obtained in the last five years. Demonstrated experience developing robotic systems using ROS 2
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across