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Field
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imaging, genetics, omics, EHR data, and clinical outcomes. Ongoing work builds on deep-learning phenotypes from cardiovascular imaging at population scale and extends toward myocardial tissue remodeling
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to appointment start date We welcome candidates with deep expertise in one or more relevant areas, and a strong desire to learn across disciplines: Computational Biology: Spatial/single-cell omics, bioinformatics
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03.04.2024, Academic staff Postdoc Green Hydrogen Financing (m / f / d) at the TUM School of Social Sciences and Technology, Professorship for Public Policy for the Green Transition
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Postdoc Appointment Term: The initial appointment is 9–12 months (typically 12 months); the second and subsequent years are awarded upon demonstrating satisfactory progress in the first year and assuming
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QUALIFICATIONS: PhD in computer science, electrical/biomedical engineering, statistics, applied mathematics, or a related field. Strong track record in machine learning/deep learning with imaging data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
), expertise in computational modeling (such as Molecular Dynamics, virtual screening, free energy calculations and Deep Learning), successful experimental collaboration experiences and excellent communication