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research environment for biophysics. Our group combines molecular dynamics simulations with machine learning techniques to understand how proteins, biomembranes, and small drug-like molecules interact
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if they demonstrate strong relevant skills. Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming. Exposure to machine learning / data-driven modelling and/or
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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research group “Machine Learning for Biomedical Data” led by Prof. Dominik Heider and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction. The CRC 1748 involves
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publications and present them at well-known international conferences and workshops. Your profile M.Sc./M.Eng. Degree in telecommunication engineering, signal processing, machine learning or a closely related
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should hold a Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, Data Science, or a closely related field Solid background in machine learning and natural
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with
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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning