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Field
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that incorporate latest machine-learning algorithms). Furthermore, the successful candidate will collaborate broadly with the other members of IO and CFN, leveraging their expertise in design and fabrication
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Description NREL is seeking a postdoc to design, train, and analyze the AI/ML and control algorithms for hybrid energy systems including industrial systems, building controls, and advanced energy systems
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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R, algorithms and systems designed to extract insights from large datasets. Experience with integrating and analyzing diverse data sources, such as biodiversity (e.g., the IUCN Red List of Species and
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Nordisk. What you can expect As a Postdoc, you will be at the heart of a vibrant and collaborative research environment that includes: Fellow Postdocs, PhD students and master’s students working on related
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computational models and systems using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. Major Duties
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of machine-learning algorithms for unmanned aerial vehicles; dissemination of the results in international conferences and journals; proposal writing for external funds. Your profile The successful candidate
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems