191 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" uni jobs in Belgium
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Leinders, tel.: +32 14 33 31 40, mail: gregory.leinders@sckcen.beSCK CEN Co-mentor: Dr. Frédéric Jutier, tel.: +32 14 33 32 24, mail: frederic.jutier@sckcen.be Where to apply Website https
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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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: Belgique Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/134945 Requirements Specific Requirements Position details We are seeking a highly motivated PhD student to join this
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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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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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available1Company/InstituteEcole de Santé PubliqueCountryBelgiumGeofield Contact City Bruxelles Website https://esp.ulb.be/ Street Route de Lennik 808 - CP598 Postal Code 1070 E-Mail jelle.van.cauwenberg@ulb.be
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the city of Antwerp, the capital of Flanders, offering a culturally and historically rich, and ethnically diverse environment. For information about the lab https://mancusolab.sites.vib.be/en For information
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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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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