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Field
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degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in
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of physics-informed machine learning proxy models for large-scale CO₂ storage. The project addresses the significant computational burden of numerical simulation and optimisation of large-scale CO₂ storage
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fuels (hydrogen, methanol, ammonia), simulation tools for marine engines and/or fires due to fuel leakages, data analysis methods and their applications for ships, sufficient understanding of appropriate
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necessary to consider also the control methods through which it is possible to increase both machine efficiency and controller efficiency. The overall goal of the project is to develop the control methodology
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innovative methods to solve real-world problems whilst delivering significant academic contributions. The post is full time, fixed term for 24 months. You will be based in the newly renovated Stephenson
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possess superior scientific and numerical skills, as well as meticulous attention to detail and accuracy. Spanish language proficiency is preferred. About the Kinder Institute for Urban Research The Kinder
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the UK Lead on participant recruitment and conduct qualitative research using participatory methods and semi-structured interviews Analyse data, contribute to co-design workshops, write reports, and
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Program (PRR Workplan: (1) Numerical simulation of the behaviour of connections between prefabricated concrete walls using finite elements; (2) Case study analysis; (3) Processing and analysis of results
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skills. Preferred Qualifications: Prior research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods