479 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Belgium
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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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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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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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, proteomics, microscopy, structural biology, technology development, and bioinformatics at VIB-UAntwerp Center for Molecular Neurology (https://cmn.sites.vib.be/ ), at VIB (https://vib.be/ ), and at UBirmingham
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image/signal processing, particularly in computer vision.Strong programming skills and experience with at least one deep learning framework e.g. TensorFlow or PyTorchFamiliarity with machine learning and
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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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perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural language processing and large language models hybrid, interpretable
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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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will remain confidential. More details on gender and diversity policies are available at: https://www.ulb.be/fr/l-ulb-s-engage/diversites Work Location(s) Number of offers available2Company/InstituteULB
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affected by warping, addressing both audio analysis and synthesis tasks. The methodological scope spans stochastic signal processing and machine learning, including hybrid physics‑guided and data‑driven