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deploy these technologies in the industry context without the need for big datasets. You will gain from the experience in numerous ways, whether it be transferable skills in the technical area of
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networks by analyzing their dynamical systems and probabilistic asymptotic behavior, improving and generalizing diffusion-based generative AI using insights from numerical and stochastic analysis, and making
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predictions to conventional continuum predictions to understand the relationships between the different theoretical frameworks. The analysis will be accompanied by detailed numerical computations in
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on numerical aspects of the network model analysis. Being part of the wider Mathematical Neuroscience research theme within the School of Mathematical Sciences which currently includes 7 members of academic
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-speed cameras (in a newly renovated lab dedicated to our research group). A significant component of the analysis will include image processing, including data-driven methods and machine learning. You
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flow regime ranging from steady laminar to unsteady turbulent configurations, there is also potential to extend the analysis to compressible flows and structural analysis. This research is highly
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fluid dynamics (CFD) simulations, Finite Element Analysis, manage and execute the procurement of the build, run the aerothermal testing and process and communicate the results. The skills, qualifications
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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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, 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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to enhance the UK’s energy system resilience through a whole-system analysis approach. Building on the proven WeSIM model, RENEW will upgrade its capabilities to incorporate electrified district heating and