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to defining specifications and requirements for the FDI system • Development and evaluation of model-based and machine learning-based FDI algorithms, in close exchange with relevant stakeholders • Close
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declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible. About the team Join the Responsible Machine Learning (ML) Group at the Faculty
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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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. Joerg Hoffmann at University of Saarland. 2) 1-2 PhD students working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on the topic of developing novel approaches for learning, compressing, and
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applying different multivariate calibration strategies and machine learning approaches. Finally, the variation of the sensor measurements will be studied in relation to the cow’s health and combined with
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the foundations for this. The ideal candidate should hold a PhD in a relevant specialist subject or is about to submit, or should have equivalent experience. A background in machine learning applied in
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experimentally explore their fascinating dynamics. This project consists of an experiment-theory collaboration in which a PhD candidate in Experimental Nano & Ultrafast Magnetism, supervised by Prof. Bert Koopmans
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for the scholarships. Experience in Machine learning and/or finite element modelling would be preferable. The successful candidate will be supervised by Associate Prof. Hafizah Binti Ramli, Dr Sabrina Fawzia, and
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily