24 machine-learning-"https:" "https:" "https:" "RAEGE Az" PhD scholarships at KU LEUVEN
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experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) would be a benefit; The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under
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to learn are essential. Solid programming skills in Python and/or R; experience with reproducible workflows (Git, Snakemake/Nextflow, containers) is a plus. Interest in cancer biology, tumor microenvironment
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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. dr. Erwin Dreesen, mail: erwin.dreesen@kuleuven.be Where to apply Website https://www.kuleuven.be/personeel/jobsite/jobs/60606922?hl=en Requirements Research FieldPharmacological sciencesEducation
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more detailed description of the main research topics of the pure mathematics research groups at KU Leuven can be found here: https://wis.kuleuven.be/methusalem-pure-math/methusalem-lines-of-resear
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the requirements here: https://www.kuleuven.be/english/study/apply/language-requirements/engli… ). Applicants can be of any nationality. We encourage applicants who have African language skills and
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: https://www.kuleuven.be/english/study/apply/language-requirements/engli… ). Applicants can be of any nationality. We encourage applicants who have African language skills and relevant experience outside
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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. Pieter Rauwoens, mail:pieter.rauwoens@kuleuven.be or prof. Sandra Soares-Frazao, mail:sandra.soares-frazao@uclouvain.be Where to apply Website https://www.kuleuven.be/personeel/jobsite/jobs/60598708?hl=en
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning