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project TARGETWISE. The candidate will be responsible for conducting machine learning omics data analysis within the computational team. The details on responsibilities, obligations and rights
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Do you have an ambitious research idea that you wish to pursue in your postdoc? Are you interested in theoretical or computational science in an interdisciplinary environment? Then HITS is the
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical
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of translating our insights into actionable strategies for pediatric care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi
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, such as scientific computing and scientific machine learning, and promote interdisciplinary collaboration among the disciplines of mathematics, computer science, and engineering. Candidates with strong
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) independence to lead a project as well as willingness to work in a team; (g) an open mind to learn new methods from junior researchers and collaborators; (h) good scientific presentation and writing skills
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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to present their work and learn from others. Fellows are also encouraged to engage with colleagues and attend departmental seminars. Applicants must demonstrate outstanding scholarly achievement and promise
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, approximation theory, high-dimensional probability theory, mathematical aspects of machine learning, compressed sensing Secondment: Univ.-Prof. Dr. Philipp Grohs (University of Vienna, Faculty of Mathematics