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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
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. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
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track-record in first-author scientific publications for Postdoc applications Experience with data-driven machine learning methods for modelling (PINN, Sparse Symbolic Regression methods) High willingness
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)! Tübingen has a long history of academic excellence (founded in 1477; DNA was discovered here ; linked to 11 Nobel laureates) and is an innovation center in medicine and machine learning. About Eberhard
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Neurobiologie (ZMNH) Main tasks You will join the Institute of Medical Systems Biology and the bAIome Center for Biomedical AI (baiome.org) to complement our lively and enthusiastic team of machine learning and
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming