41 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" uni jobs in Belgium
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should be exclusively submitted through the consortium webpage https://raptor-consortium.com/recruitment/ RAPTORplus aims at adapting proton therapy treatment with multi-modality image information acquired
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, Mechanical), Computer Science, Applied Math/Statistics, Physics—or related. Candidates who will graduate in the near future are also welcome to apply. Strong foundation in machine learning/deep learning and
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activities, I have gathered experience with machine/deep learning, and can demonstrate a strong affinity with these fields. Prior experience with computer vision is a plus. I am proficient in Python and am
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experimental workflows for generating and automating the acquisition of high-quality training datasets for machine learning models. Provide training to students on new technologies, protocols, and best practices
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Software Testing and Analysis Machine Learning and Large Language Models Web Systems and IoT Systems The candidate must possess strong programming skills in Java or Python and is willing to learn all
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to that present in the LMC is recommended, e.g. on AI/Machine learning in drug design, assay development, bioconjugate chemistry, fragment-based discovery, DNA-Encoded Libraries (DEL), sustainability aspects, etc
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consistency outside the training domain. This PhD research is envisioned to result in a breakthrough in the application of machine learning methods to fire engineering problems, by ensuring compatibility with
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engineering Researcher Profile First Stage Researcher (R1) Country Belgium Application Deadline 31 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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well connected to the machine and transportation, high precisionindustriesand I am eager to learn how academic research can be linked to industrial innovation roadmaps. During my PhD I want to grow
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the lifecycle of industrial systems. As machine learning sees broader adoption, companies are increasingly required to ensure the safety of machine-learning-enabled systems. The reliance on training data and the