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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large
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optimization. In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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) to work on the development of an Amazonian Early Warning System (AmEWS) integrating Earth Observation data, process-based ecosystem models, and advanced machine learning approaches. The position is embedded
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Water Fluxes. Your tasks Build hybrid models, process-based and deep learning models, to capture ecosystem flux dynamics across space and time Develop generalizable models robust to climate variability
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funding via an employment contract with the Max Planck Institute for the History of Science (MPIWG) or with Technische Universität Berlin and will have the possibility to teach. Two employment contracts
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Degree PhD Learning Sciences (alternatively Dr phil, Dr rer nat, Dr med; not recommended for international students) Course location München Teaching language English Languages Courses are held
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the opportunity to learn about their new place of study and for them to get to know the other students. Orientation activities also include an intercultural training and a walking tour of Berlin. Technische
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Nanoscience: Synthesis of Carbon-Nanostructures on Inert Surfaces Biophysics: Learning
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Jyvaskyla University of Manchester Kone Oyi The candidates will have the opportunity to visit various partners in the network, supported by a mobility allowance. At the Chair of Machine Learning for Complex