78 machine-learning "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" uni jobs at Ghent University
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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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their results in the context of the clients. Analyses will be conducted with several software packages for statistical data analysis (possibly including R, SAS, Python, …). The new colleague may acquire new
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immunology and rheumatology research. For more details see: https://www.irc.ugent.be/groups/elewaut-lab You will be supervised by experienced investigators with a strong international track record in
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(Lies.Lahousse@UGent.be , +32 9/264 81 14). Do you have a question regarding the online application process? Please read the FAQ or contact us via selecties@ugent.be . Where to apply Website https
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the FAQ or contact us via selecties@ugent.be . Where to apply Website https://academicpositions.com/ad/ghent-university/2026/assistant-pharmacoepidem… Requirements Research FieldBiological sciencesYears
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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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of statistics, computer science and/or machine learning Interest in biology or molecular biology, microbial ecology Proficiency in programming languages such as Python, R and/or C++ as well as Linux systems
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on various aspects of (micro)palaeontology, which will constitute a rich and exciting working environment for the successful candidate. https://www.ugent.be/we/geologie/en/research/organization/palaeontology
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. For more information about this vacancy, please contact prof. Petra Van Damme (petra.vandamme@UGent.be , +32 (0)9/264 51 29). Where to apply Website https://academicpositions.com/ad/ghent-university/2026
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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