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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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. Background: Detecting deception is an important but challenging task for numerous organizations worldwide including in the UK (e.g., Probation Service, Police). Whilst there are different approaches, the most
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of these dynamics. At the heart of this project is an attempt to develop deeper understanding of these phenomena in terms of the flow physics and to provide practical modelling methods that correctly represent
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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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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
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in public health. Key project aims: (1). Determine which chemical exposures present the greatest risk for development of metabolic disease. Using knowledge synthesis methods to evaluate existing
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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Applications are invited to undertake a 3.5 year PhD programme to explore new laser surface engineering methods for functionalising the electrochemical performance of solid-state batteries. In
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Engineering, Applied Mathematics, or Physics, is required from candidates. Desirable skills: mathematical modelling, numerical methods, good programming skills in any language, self-motivation and a passion for
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using and further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use the MMI apparatus, gaining knowledge