205 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" uni jobs in Belgium
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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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, or compensation if you come by bike; Cost-free hospitalisation and group insurance with guaranteed income; The space to form your job content and continuously learn via VUB LRN; Excellent facilities for sport and
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the links. Questionnaire: https://forms.office.com/e/4ttu1GtVmu Academic dossier : https://www.fundinginstitutional.com/opportunities/9000002408 (please upload this document under ‘Degrees and Certificates
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the links. Questionnaire: https://forms.office.com/e/206EF9c20u Academic dossier : https://www.fundinginstitutional.com/opportunities/9000002408 (please upload this document under ‘Degrees and Certificates
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in autonomous systems such as ground and aerial vehicles, and mobile robots. This includes: formulating and solving long-standing multiterminal information theory problems using modern machine learning
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, optimization, and continuous learning in a rapidly evolving field.Desirable: experience with multi‑omics datasets, single‑cell or spatial technologies, or basic scripting skills (R/Python). Our offer Access
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development, optimization, and continuous learning in a rapidly evolving field. Desirable: experience with multi‑omics datasets, single‑cell or spatial technologies, or basic scripting skills (R/Python). Our
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Where to apply Website https://jobs.vito.be/o/phd-position-coupling-electrochemical-nitration-and-redu
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research groups within CCB (Learn more: Bioinformatics & AI ). Your Role We are seeking a skilled computational biologist to organize and deliver training on bioinformatics and AI for the CCB research
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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