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group. The position is associated with a project funded by the UK Engineering and Physical Sciences Research Council. The post holder will be expected to display the initiative and creativity, together
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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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dedication to promoting diversity in STEM academics and fostering a nurturing and encouraging atmosphere. The Role This post holder will be responsible to develop next generation non-terrestrial networking
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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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molecules in optical lattices. The project is a collaboration with researchers in Paris and Warsaw. The post holder will work directly with Prof. Simon Cornish (Physics) and Prof. Jeremy Hutson (Chemistry
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or postgraduate teaching programmes or by membership of departmental committees, etc. and by attending relevant training and development courses. Any other specific responsibilities. The post-holder is employed
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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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to join the existing programme team. One of these roles is a Post Doctoral Research Associate (PDRA), who will work to the Centre full time; supporting the Centre Director, Professor Carlene Firmin, and the
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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