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Field
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PhD Studentship: Improved Heat Transfer Understanding via Conjugate Heat Transfer, Co-Simulation and AI Approaches Research has shown that the development of gas turbines is critical to the success
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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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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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: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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emissions and economy, there is limited knowledge on how this will affect the rate of degradation of the road infrastructure. You will conduct laboratory investigations, possible field work and numerical
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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
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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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of a DG method for solving the hyperbolic BBGK equation has not yet been fully exploited by the rarefied gas dynamics community which continues to rely heavily on the costly direct simulation Monte Carlo
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tools (e.g., drones, 3D mapping) for high-resolution geological mapping and rock mass quality assessment. Develop and calibrate numerical models using field data and case studies to simulate various
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partnership with the Odysseus Space company as part of the Simulator development for the optical slant path program. This PhD project aims to develop an analysis tool for the optical ground-to-satellite links