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application of cutting-edge causal machine learning methods You will further elaborate and concretise the PhD theme and research tasks at the start of the PhD in consultation with the supervisor and any co
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), consists of two main parts. First, the candidate will develop machine learning models aimed at improving the follow-up of neurocognitive function in critically ill children after discharge from the intensive
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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these fields is not required, what we are looking for is a strong motivation to learn, adapt, and apply these technologies to advance microbiome research. Profile We are looking for a driven and creative PhD
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onboarding period that includes specialized courses and hands-on training in AI and machine learning. You'll also have the chance to explore different labs and core facilities, meet fellow researchers, and
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onboarding period that includes specialized courses and hands-on training in AI and machine learning. You'll also have the chance to explore different labs and core facilities, meet fellow researchers, and
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communications Data Analysis and Management Implement and open-source proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C
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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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, and creative individual to join our team. The candidate should have a strong interest in clinical epilepsy/neurology research and be open to acquire novel skills in bioinformatics, biostatistics, and
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is focused on enabling applications through a fundamental understanding of material formation processes and properties. KU Leuven (University of Leuven) has been a center of learning for nearly six