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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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Fraunhofer IGD and the FBN team to safeguard an efficient collaboration and communication between behavioural biologists and computer scientists. The project is part of the KI-Tierwohl project (https://ki
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 6 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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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 the opportunity to work with
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Job description: DESY The CMS Quantum Computing group develops generative machine learning models for detector simulations, specifically the simulation of showers in calorimeters: Proof-of-principle
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and simulation Prior experience with particle accelerators and/or FELs is highly desirable Familiarity with machine learning techniques is a plus but not necessary Excellent command of English is
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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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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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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management skills • Experience with qualitative or mixed-methods research • Familiarity with AI, machine learning, neurotechnology, or robotics research contexts • Interest in science policy, governance