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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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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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Python or R A willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international level
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control engineering, optimization algorithms Control of drones and flight experiments as well as knowledge in AI / Machine Learning would be an asset Outstanding academic records Teamworking experience, e.g
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and