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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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Description The Soft Matter Physics Group at the University of Tübingen is searching for a PhD student / doctoral candidate in Physics, Machine Learning and advanced X-ray and neutron scattering (m
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looking for a PhD Student (f/m/d) in Machine Learning for Quantum Computing and Simulation of Quantum Matter. The Scope of Your Job The successful candidate (f/m/d) will be part of the interdisciplinary
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI
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related field Sound knowledge in the field of artificial intelligence and machine learning Ideally experience with knowledge graphs, semantic search, graph neural networks (GNNs), explainability
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chemistry, theoretical chemistry, molecular dynamics, data science, and machine learning are beneficial. What we offer: We offer a position with a competitive salary in one of Germany’s most attractive
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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of computational approaches to large scale simulations Basic knowledge of (geo)chemical processes and machine learning will be of advantage Expertise in Machine Learning approaches, ideally beyond neural networks