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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science, applied mathematics, or a closely related field. Experience or interest in
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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics
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biomedical engineering). Flexible start date! What You’ll Need A first-class or upper second-class honours degree (or equivalent) in Engineering, Physics, or Applied Mathematics. Experience in coding and CFD
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Description/content The PhD programme in Physics is part of the Georg-August University School of Science (GAUSS), which offers a structured doctoral education and promotes excellent research in mathematics and
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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physics, biology, chemistry, and mathematics at an undergraduate level. In addition, the courses also require basic knowledge in the social sciences. Courses are offered by academic staff at both
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models and/or model components. They apply mathematical models to answer specific research questions, analysing dynamics and processes in the natural system and its interaction with the anthroposphere
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interdisciplinary training for PhD students. The programme supports you in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life sciences
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of systemic neurosciences: single cells to complex systems system analysis to mathematical modelling perception and cognition to mind and neurophilosophy biology to technical solutions Additional support is
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with high-dimensional, often noisy, data sets; and mathematical modelling approaches that reduce the dimensionality of parameter spaces and produce mechanistically realistic, experimentally testable