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Field
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incorporating time-dependent source depletion. (4) Reducing uncertainty in groundwater risk assessments through refined numerical methods. (5) Applying the improved model to real-world groundwater contamination
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energy through more efficient design of piled foundations. The PhD student will focus on grain scale mechanics experiments and numerical modelling using the Discrete Element Method (DEM) with the aim
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remains limited, with numerous fabrication challenges yet to be addressed. This collaborative project in close collaborration with partners at ETHZ and TU Delft aims to bridge the gap in current medical
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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with knowledge and interest in structural dynamics, finite element analysis, programming and numerical methods. Applicants are expected to have achieved or be about to achieve a First-class honours MEng
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acquisition and statistical methods is an advantage. Experience with numerical simulations (FEM). You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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-of-the-art in silico protein structure prediction methods and in vitro screening approaches will be used to systematically elucidate the amyloidogenic potential of the gut metaproteome. Specifically, gene
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise
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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research