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the top 10% of their class in MSc and BSc, and have exceptional grades; should have a background in microwave engineering and machine learning; should have strong communication skills and be fluent in
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signal processing and machine learning algorithms, have strong interpersonal skills and the ability to work in an international team. Experience in wireless communication and networking fundamentals is a
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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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familiar with data science and machine/deep learning toolkits. Experience with model deployment and the usage of MLOps tools (Dockerization, CI/CD pipelines, edge infrastructure, etc.) is a plus. As a PhD
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. For instance, it might be more productive to develop very fast treatment workflows without online adaptation to maximize patient throughput on an expensive proton therapy machine, instead of performing
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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and/or the CAD/CAM process is a plus. I am proficient in Python and am familiar with data science and machine/deep learning toolkits. As a PhD researcher at KU Leuven, I perform research in a structured
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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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for an FWO PhD scholarship to further develop the initiated research (if eligible). Advantages strengthening the candidate’s profile, but strictly required: Experience with machine learning or system
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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