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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 1 month ago
) is a German-Polish research center for data-intensive digital systems research. CASUS is looking for a Postdoctoral Researcher (f/m/d) in Machine Learning and Surrogate Modeling for Geochemical Systems
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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
General Summary of the Position Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display/20062865 ). The Zhou Lab at UMass Chan Medical
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to eligible team members. Learn more at https://hr.duke.edu/benefits/ Minimum Qualifications Education See job description for education requirements. Experience See job description for requirements. Degrees
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About The University of Texas at El Paso: ABOUT THE ECE DEPARTMENT: The Department of Electrical and Computer Engineering at UTEP (http://ece.utep.edu) offers two undergraduate and three graduate
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About The University of Texas at El Paso: ABOUT THE ECE DEPARTMENT: The Department of Electrical and Computer Engineering at UTEP (http://ece.utep.edu ) offers two undergraduate and three graduate
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-intensive physics with nuclear/particle aspects, advanced detector R&D, machine learning and AI and emerging computational methods in quantum computing. The position is intended for an excellent and broadly
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years of postdoctoral research experience is preferred. Strong background in big data analytics, machine learning, and multi-omics. Strong track record of high-quality research, demonstrated by
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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, spectropolarimetric inversion techniques, and machine-learning–based approaches, for the physical interpretation of solar images and spectral profiles. Special consideration will be given to applicants with experience