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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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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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citation record must be focused on AI; or alternatively (B), machine learning engineers with an AI-focused PhD and demonstrated 2-year industry experience in AI development Applicants must have in-depth
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of data analysis, time series analysis, machine learning and algorithm development. have knowledge on machine learning with Python or MATLAB. are very fluent in English, both spoken and written. possess
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interdisciplinary research project at IDLab-MEDIA (https://media.idlab.ugent.be/ ), UGent – imec, aimed at advancing the state of the art in motion capture, sensor fusion, immersive media, and 3D computer vision
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occupational asthma, dust lung, auto-immune diseases, cancer, etc). · Strengthens existing research lines and brings complementary and/or additionally new expertise by working closely with the members
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concepts such as digital twins, learning from human interactions. In the domain of efficiency and sustainability, projects relate to a.o. the development of novel electrical machines, engineering of