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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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RAP opportunity at National Institute of Standards and Technology NIST Incorporating Theory and Domain Knowledge into the Machine Learning of Polymeric Systems Location Material Measurement
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Application documents: 1) Brief cover letter, explaining your motivation for applying, 2) Detailed curriculum vitae (including your email address), 3) Complete transcript of grades from all your university-level studies. We do not ask for more information/documents at this point (but you can...
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RAP opportunity at National Institute of Standards and Technology NIST Combining Theory, Simulation, Machine Learning, and Autonomous Experiments for Industrial Formulation Discovery Location
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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NIST only participates in the February and August reviews. The NIST Electron Microscopy Nexus is an internal shared-use facility with 13 electron microscopes (S/TEM, SEM, dual-beam instruments), and our data infrastructure is set up so that all microscopy datasets from all users are collected...
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Candidates (f/m/d) in Machine Learning Applications to Sustainable Energy Management. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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"A picture is worth a thousands words"... or so the saying goes. How much information can we extract from an image of an insect on a flower? What species is the insect? What species is the flower? Where was the photograph taken? And at what time of the year? What time of the day? What was the...