114 machine-learning "https:" "https:" "https:" "https:" "https:" scholarships in Germany
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profound knowledge in key areas of infection biology. At the same time, students learn to examine their research findings critically and to present them to an audience. In the last two half-year modules
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in the following areas is also a bonus: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show initiative, creativity, and to work independently Excellent
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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equivalent university degree in psychology or a related discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking
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interaction-rich scenarios. Ideal applicants will have a strong M.Sc. in machine learning, control, or safety, and hands-on experience with robotics. Apply now: https://lnkd.in/dNjmv835. Deadline: ASAP. We
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discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking technology and/or computer graphics as well as interest in
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environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods to challenging problems in mass spectrometry. Independent and cooperative working in