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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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knowledge gaps. The project involves both linear and nonlinear dynamics modelling and analysis, as well as experimental testing. An equivalent test structure will first be constructed in the vibration
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-equilibrium conditions. The project is a UKRI/NSF collaboration with Virginia Tech, and the use of direct numerical simulation, modelling and analysis will be complemented with experimental data from
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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of how alternative land management practices impact greenhouse-gas fluxes through the development and application of sophisticated modelling tools. The work will involve model development on the Cambridge
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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes
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grade. Generate data through comprehensive laboratory grinding tests on various rail grades to train and validate the ML model. Utilise numerical modelling to establish acceptable thresholds for surface
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physical and numerical modelling. Feel free to reach out to the project supervisors if you have any questions. Entry requirements: The ideal applicant will be enthusiastic and self-motivated with a first
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative