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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 27 days ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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using experimental data from laboratory setups Validate models through targeted measurements and structured testing procedures Analyse loss mechanisms and system efficiencies under various operating
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(partly) A large research campus in the countryside, which offers the best opportunities to network with colleagues and to do sports outside of work Targeted services for international employees, e.g
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) A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work Targeted services for international employees, e.g
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possible means for networking with colleagues and pursuing sports alongside work Targeted services for international employees, e.g. through our International Advisory Service In addition to exciting tasks
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Christmas and New Year) Targeted services for international employees, e.g. through our International Advisory Service In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a
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family-friendly corporate policy Flexible work (location) arrangements, e.g. remote work Targeted services for international employees, e.g. through our International Advisory Service In
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scale and the coastal areas, up to, and including, development and demonstration of climate services. RIVIERADE will target three European Seas (Baltic, Black, Mediterranean), to produce data and
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) investigates the pathobiology of human-pathogenic fungi and identifies targets for the development of novel natural product-based antibiotics. The research group Microbiome Dynamics (https://www.leibniz-hki.de