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and numerical modelling of natural clays at both laboratory and field scale. We are active members of the ALERT Geomaterials network and other international committees. Our diverse and international
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comfortable applying analytical thinking to explore research questions and have programming experience (preferably in Python). You have an affinity with coastal numerical modelling, e.g. AeoLiS, XBeach
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in device behaviour will be explored to minimize false positives Scalability and Real-world Deployment: This research project will address the scalability of the proposed detection system to handle a
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linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
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of Electrical Engineering . You will be supervised by senior researchers with expertise in robotics, machine learning, automatic control, and optimization. The group leads and participates in numerous
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to extract information about the rotational orientation dependence of the gas-surface reaction, as well as performing numerical simulations to determine how best to perform the measurements to maximise
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:100% of the full-time weekly hours Tasks: The PhD student will be responsible for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs
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renovation construction work · evaluate (numerical or data driven) solutions for automated coordinated planning · develop and evaluate self-learning interactive visualisation technologies