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and optimisation but not limited to data driven monitoring for control and operation. You must have a good master’s degree in electrical engineering, with Power and Control Engineering major
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to the proposed area of research. You should have experience in finite element modelling and data-driven multiphysics approaches. Fluency in Python, Fortran, MATLAB and/or C/C++ and prior work with common finite
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levels. The candidate is expected to: Build models embedding dynamics and flexibility of process operations and related supply chains Combine analytical and data-driven surrogate models in optimization
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, data analysis, and modelling, in collaboration with the University of Southampton and the National Physical Laboratory (NPL). With a background in Materials Science, Physics, Chemistry, or Engineering
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, Chemistry, or Engineering, you will be an enthusiastic and self-driven researcher able to plan and deliver high-quality scientific work. You will hold (or be close to obtaining) a PhD/DPhil in a relevant
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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programmes in wind farm and power transmission system model, analysis, control and optimisation but not limited to data driven monitoring for control and operation. What we are looking for: You must have a
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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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wind farm and power transmission system model, analysis, control and optimisation but not limited to data driven monitoring for control and operation. You must have a good master’s degree in electrical