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experience with scientific computing, data analysis, machine learning and/or AI You have an interest in environmental sustainability and pharmaceutical production Considered a plus: You have experience with
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Python or R A willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international level
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data
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background in machine learning, including Natural Language Processing. You have excellent coding skills in Python; hands-on experience in deep learning frameworks such as PyTorch or Tensorflow is a plus You
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of the professors of the Machine Learning group mentioned above, that is, Prof. Jesse Davis, Prof. Luc De Raedt, Prof. Tias Guns, Prof. Giuseppe Marra or Prof. Hendrik Blockeel. You will be part of a dynamic team
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biomass remote sensing, crop modeling, data assimilation and machine learning Supervise master thesis students For PhD students: follow training in line with the doctoral school requirements Where to apply
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combine EMI footprints, which capture normal variations through characteristic curves and statistical distributions, with state-of-the-art machine learning and deep learning techniques (e.g., one-class
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regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will be expected to seek independent