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Field
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utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images
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. Profile You hold a Master's degree in Mathematics completed before the start date. You have a strong interest and background in one or more of the following areas: mathematical analysis, numerical analysis
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and information theory, statistical analysis, optical communication systems. Knowledge of mathematical methods, linear algebra, mathematical physics, and numerical analysis are an asset. The candidate
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crystal materials to support the complex nature of future designs. To support this, we are already testing material at Swansea as part of the ATI funded ‘HOTLINE’ project. RR will be doing some analysis of
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, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
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of rotation and stable stratification. To understand the origins of these energy transfers, we will perform direct numerical simulations using the global spectral method with extremely high precision to capture
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models or forecasting and econometric analysis Proficiency in one of the major programming languages such as Python A collaborative team player with a desire to make a personal impact within our
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related field. Interest in energy markets, digital technologies, policy analysis, or regulatory frameworks. Strong quantitative and qualitative analytical skills. Proficiency in statistical software such as
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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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-signalling/ ), led by Prof. Alexander Skupin, which focuses on multiscale analysis of brain disorders with a focus on Parkinson's and Alzheimer's disease, and epilepsy by combining experimental and