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or equivalent experience in machine learning or a related quantitative field (Computer Science, Artificial Intelligence, Statistics, Mathematics, Physics, Computational Biology/Chemistry). Candidates will be
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research project "Plasma state classification and abnormal event detection in nuclear fusion devices with Symmetrized Dot Pattern and Machine Learning applied to time series of tokamak plasma radiation
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on psycho-social and behavioural issues and how they affect the design and usability considerations related to clinical applications and devices. Students will learn about how artificial intelligence can be
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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, machine learning or neuronal population analyses would be an advantage. Specific Requirements We are looking for a candidate with a strong interest in scientific software development and quantitative
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advanced artificial intelligence models (cellular automata, machine learning) of diffusion processes at high temperatures Performing in-depth microscopic and phase analyses of the oxides formation in
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: A PhD in Machine Learning, Computational Biology, Bioinformatics, Computer Science, Statistics, Data Science, or a related field is preferred. Candidates must also have impeccable ethics and integrity
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium. Duration and start date: 48 months, full-time