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to the PhD project ie. processing and analysis of dietary intake data, statistical analyses (eg. linear mixed models) as well as evaluation of child growth and body composition data. Relevant publications
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
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, and contribute to identifying tumor vulnerabilities that may become future therapeutic targets. What we offer: A dynamic and interdisciplinary research team with expertise in cancer biology, statistics
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structures, etc to solve challenging problems is required (there will be a practical coding assessment during recruitment) A solid mathematical foundation is required (multivariable calculus, linear algebra
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understanding of deep neural networks by exploring the human-understandable meanings of learnt features, the evolutionary dynamics of these features across network layers, and the architectural designs