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Field
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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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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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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 generated in the scattering
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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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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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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
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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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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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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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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