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Description Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms? Job description Online learning algorithms achieve robustness often at the expense
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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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to the sensors. This type of edge computing has been made possible by advances in deep learning methods on embedded hardware, e.g., TensorFlow Lite for microcontrollers. You will support our ambitions by working
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally
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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related
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You hold a PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning
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Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through coursework or research projects. Our offer a position for 18 months, with an extension to a total of four years upon
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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision