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willingness to engage in interdisciplinary cooperation as well as cooperation with existing research groups in mathematical physics, dynamical systems, data science, machine learning, control theory
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
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federated learning for decentralized AI model training for quality assurance of machining processes within the project »FL.IN.NRW «. A custom dataset composed of machine internal signals and external sensor
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interdisciplinary cooperation as well as cooperation with existing research groups in mathematical physics, dynamical systems, data science, machine learning, control theory, optimisation, or numerics are expected
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). Knowledge of Docker and machine learning is considered a plus. Knowledge of standard bioinformatics tools for analyzing and interpreting Next Generation Sequencing data. Excellent oral and written
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: machine learning, data analysis, energy technology Experience with common deep learning and data analysis frameworks (e.g., PyTorch, Numpy, Pandas, sklearn, etc.) Independent, structured, and reliable way
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Biology, Computer Science or related studies) Experience in Python with PyTorch (or equivalent) programming Experience in sequencing data analysis Basic knowledge in machine learning Experience with linux
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necessary records and entering data onto computer systems. Your role: Animal husbandry, care and use Provide day to day routine husbandry, maintenance and care of LAR fish colonies (zebrafish, medaka
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of research focus include machine learning/learning analytics, multimodal assessment, adaptive learning in online settings, and the role of self-regulation in learning with AI. Close networking with
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pursuing a Bachelors or Masters degree in engineering, physics, mathematics or computer sciences and related discipline and/or an unaffiliated recent graduate with a Bachelor/Master degree from