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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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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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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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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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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
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how a novel machine learning-based methodology leveraging reinforcement learning with human feedback and multi-objective optimisation can be realized to generate new and even improve existing work plans
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guaranteed. Given the real-time nature of these large complex infrastructures, machine learning techniques can complement more deterministic algorithms to guarantee a reliable operation of the system
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). Advantages strengthening the candidate’s profile, but strictly required: Experience with machine learning or system optimisation; proficiency in Python or MATLAB. Previous authorship or co-authorship