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to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
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optomechanical interaction for the polaritons and mechanical modes in GHz range. The project is carried out in close collaboration with leading experts in semiconductor cavity optomechanics in Berlin, providing
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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
models. You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral students. You will closely collaborate with Prof
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also have a chance to instruct Doctoral researchers and Master thesis candidates and develop towards a more independent position. Background and expertise We are looking for candidates who understand
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in close collaboration with stakeholders across research and education. About 45 doctoral candidates and 350 master’s students graduate from the school every year. The school is home to 700 staff