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-in time of new infrastructure is years, if not decades. The combination of conventional linear optimization energy models, which cover for the major part of the system, and the inclusion of partial
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the effects of local policies such as building refurbishment strategies, and examining the role of hydrogen in facilitating sustainable energy transitions in cities. Join our dynamic institute to tackle climate
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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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of technologically relevant oxide thin films. In particular, the combination of state of the art non-linear optics monitoring and electron spectroscopy in situ allows investigating the dynamics
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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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of the structural performance, and they should be able to accommodate dimensionality and complexity reduction of their associated non-linear time-variant nature. (ii) And there is a need of developing measures
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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