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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 5 days 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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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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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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collaboration with Philips Medical Systems. You will be part of a diverse and passionate research team of academic staff, PhD candidates and Postdoctoral researchers in the Computer Engineering group. Curious
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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
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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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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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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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willing to work in a collaborative environment. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high