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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning
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for the project team. We are looking for a student with a strong background in at least one of the following subjects: statistics, machine learning, discrete optimization, operations research. A strong interest in
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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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experimental data to test hypotheses or measure phenomena, in online, lab and /or field settings. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to
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expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis. The supervisory team will consist of dr. Maarten
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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development in coordination with the interests of its scientists. As such, there are plenty of opportunities to learn new skills, expand your knowledge, collaborate across disciplines, and experiment in a
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or computational neuroscience/machine learning. You possess solid programming and software engineering skills. You have excellent written and spoken English skills. You are a proactive team player, who enjoys