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tools. We predict chemical reactivity and molecular properties using machine learning, artificial intelligence, computational chemistry, and cheminformatics. Our ultimate goal is the computer-aided design
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to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. Questions regarding the position should be directed to Simon Schlapansky, by email at
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Computer Science, Artificial Intelligence, or related field. Proven experience in machine learning and neural network architectures. Strong programming skills in Python and familiarity with PyTorch. Experience with
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machine, electrical, and metal industries, driving AI-powered innovation in manufacturing. The Machine Concepts research group at inspire AG, in collaboration with the Chair of Artificial Intelligence in
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would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. For recruitment services the GTC of ETH Zurich apply. About ETH Zürich ETH
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80%-100%, Zurich, fixed-term The chair of Artificial Intelligence in Mechanics and Manufacturing at ETH Zurich, led by Prof. Dr. Dirk Mohr, combines physically motivated models with advanced ML
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applications). We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. For recruitment services the GTC of ETH Zurich apply. About ETH
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, textiles, artificial intelligence, edge computing, biomedical engineering, physiology, and technology for wearable/mobile devices meet, at one of the world’s leading universities focusing on research and
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application portal. Applications via email or postal services will not be considered. We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial
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applications for a PhD position in multimodal artificial intelligence. We are looking for an exceptional candidate to join our dynamic research team in developing next-generation AI solutions that combine visual