77 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" uni jobs at Ghent University
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to contribute their expertise, experience and interests, while jointly shaping an integrated research approach that supports mutual learning, forward thinking, participatory research practices. Please consider
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for this position. For more information about this vacancy, please contact Dr. Ir. Jordy Motte (jordy.motte@ugent.be ) or Prof Pieter Nachtergaele (Pieter.Nachtergaele@UGent.be ). Where to apply Website https
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supervise research projects and acquire the necessary funds from competitive research funding channels: you have demonstrable experience in writing grant proposals; having an adequate understanding
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encouraged to apply for this position. For more information about this vacancy, please contact prof. Nele De Belie (nele.debelie@ugent.be ). Where to apply Website https://academicpositions.com/ad/ghent
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within the CVAMO Flanders Make Lab at Ghent University. The project focuses on developing machine learning models to predict manufacturability and manufacturing effort directly from CAD geometry
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physical principles into the learning process to maintain physical consistency outside the training domain. This PhD research is envisioned to result in a breakthrough in the application of machine learning
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professors, 2 postdoc researchers, and about 20 PhD students. The research for these PhD positions will be conducted in the System Software team, headed by prof. Bjorn De Sutter (https://users.elis.ugent.be
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European cities. The project explicitly embraces a broad AI perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural
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• Interest/knowledge of statistics, computer science and/or machine learning • Interest in biology or molecular biology, microbial ecology • Proficiency in programming languages such as Python
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, incorporating transient tribological changes. Creating machine-learning-based surrogate models to enable rapid efficiency and lifetime predictions under realistic operating conditions. Validating the developed