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the Department of Engineering, with a focus on digital twin modelling for marine engines and propulsion systems involving different decarbonisation technologies, as well as the development of AI-driven decision
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investigate the “Solar wind outflow and open flux”. Candidates with a strong track record in solar magnetic field modelling, and/or familiarity with solar observational data, are particularly encouraged
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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information to specialists and within the wider academic community. Experience in designing and operating combustion test rigs; developing analytical engineering models using CONVERGE or other CFD tools; and
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; supervising student projects/practicals. · Demonstrable ability to model experiments using a range of software packages, such as Python, Matlab or Mathematica. · Experience of experimental
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University Guide 2024, and ranked 4th for impact in REF 2021. Due to the reach and scope of the Contextual Safeguarding programme most team members work to a hybrid working model, with research underway in
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, including those appearing in string theory. 2. To uncover the universal algebraic structures of integrable models. 3. To construct new integrable instances of gauge/gravity duality. 4. To investigate
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University Department of Engineering, who is the overall project’s principal investigator and will be modelling and testing materials we produce. The successful applicant will be expected to lead the wet