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Field
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This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
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-dependent source depletion. Reducing uncertainty in groundwater risk assessments through refined numerical methods. Applying the improved model to real-world groundwater contamination case studies. Career
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in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
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, geomagnetism) and the development of corresponding numerical methods. We offer the opportunity to work in a small interdisciplinary research group consisting of mathematicians, computer (geo)scientists, and
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lack of CFD-grade experimental data for reliable validation of numerical methods. In parallel to the CFD research, the Thermo-Fluids group at the University of Manchester is developing a novel modular
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equations, and numerical methods. Advanced programming skills in languages such as Python, C++, MATLAB, or R. Strong academic curiosity and enthusiasm for the chosen research area. Application Process To
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and very good knowledge in quantitative and qualitative research methods Good knowledge of statistical software (e.g. SPSS or STATA or R or JASP) Strong commitment and the ability to work in a team
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asymptotic analysis and other perturbation methods, and numerical, i.e. an existing in-house code will be modified to achieve the objectives. This project offers the unique opportunity to develop strong skills
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in an engineering or related subject with experience of mechanics, finite element methods and numerical analysis. Please state your entry requirements plus any necessary or desired background A first
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Master in Experimental Psychology, HCI, UX or related fields Experience with research methods in social sciences (e.g., empirical research, experiment design) Experience in statistical methods is