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Field
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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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experimental collaborators, we build numerical and theoretical models from the ground up, revealing how the known mechanics of individual components give rise to collective behavior. Many such phenomena occur
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metrology, with expertise in quantum state discrimination and parameter estimation and/or in-process metrology and conventional optical microscopy, as well as image processing and model development
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materials, notably wear, fretting, and thermo-mechanical fatigue. Experimental studies to support these modelling activities are also of great interest to the group. Visualisation of multiphase fluids with
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Ability to actively communicate and co-operate within a larger research team is required. Experience with LINUX environments and analysing large datasets from numerical models is an advantage Experience
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Numerical Simulations of Complex Dynamic Systems, E.g. Turbulence, Hurricane. The duty of the role is within the range of: 1) developing novel algorithms on data analyses and data mining, e.g. Large Models
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region surrounding Fayetteville is home to numerous Fortune 500 companies and one of the nation’s strongest economies. Northwest Arkansas is also quickly gaining a national reputation for its focus
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processes as droplets/condensates wet membrane compartments in cells. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic
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reduction techniques and numerical methods for subsurface and surface flow processes. You will gain experience with forward hydrodynamic models developed in the Coastal Hydraulics Laboratory and contribute
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We are looking for a researcher, whose expertise lies in numerical methods for dynamical systems, to work with Dr Rachel Nicks and Professor Stephen Coombes on a Leverhulme Trust - funded project