75 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at Ghent University
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, United Nations University, ...).https://www.ugent.be/en/research/funding/globalsouthhttps://www.ugent.be/++resource++plone-logo.svg Ghent University actively participates in several development projects, financed
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of thermal energy and (hybrid) machine learning in which physics-based models are combined with data-driven techniques. Thermal cycles make it possible to meet the demand for heating, cooling, and electricity
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machine learning. This encompasses application domains such as industrial inspection, (ultra) high-definition video enhancement, smart multi-camera networks, computer vision, sensor fusion, and (medical
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to acquire them; demonstrate strong abstract reasoning skills; have a good command of English (working language). No prior knowledge of Dutch or French is required; willingness to acquire basic Dutch is
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with both experimental research and simulation software. You possess strong technical knowledge, expertise, and hands-on experience in the field of electric machines. Additional knowledge of power
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technical knowledge, expertise, and hands-on experience in the field of power electronics. Additional knowledge of electric motor design and the control of electric machines is considered an asset. You have
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researchers from IDLab-AIRO (robotic experts) and imec. Your main tasks include: Reviewing literature on decentralized control frameworks in the domain and machine learning algorithms compatible with
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contact Prof. Ine Lentacker (Ine.Lentacker@UGent.be ) and dr. Rein Verbeke (Rein.Verbeke@UGent.be ). Where to apply Website https://academicpositions.com/ad/ghent-university/2026/doctoral-fellow-departme
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, please contact Prof. Ine Lentacker (Ine.Lentacker@UGent.be ) and dr. Rein Verbeke (Rein.Verbeke@UGent.be ). Where to apply Website https://academicpositions.com/ad/ghent-university/2026/doctoral-fellow
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. Your core research tasks include: Developing advanced MBS/EHD models for drivetrain systems, incorporating transient tribological changes. Creating machine-learning-based surrogate models to enable rapid