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Role: Postdoctoral Research Associate in Numerical Analysis Grade and Salary: Grade 7 - £37,694 - £47,389 FTE and working pattern: Full-time (35 hours per week), Fixed for 4 Months Rewards and
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appointment is for one year, renewable for a second year pending satisfactory performance and availability of funding. Research topics of interest include numerical methods for scientific machine learning and
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profile with advanced skills in: - Numerical programming in Fortran, C, or C++, and Python, - Spectral methods using Fast Fourier Transform (FFT), - Code debugging. He should have a keen interest in
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for geometric PDEs, in particular for fluid problems posed on surfaces. Both theoretical analysis of numerical methods and code development. Job requirements: PhD in mathematics or applied mathematics, experience
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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to the viscoelastic behavior of polymeric foams under large deformations dynamic loadings. The objective is to simulate, using the finite element method, the response of these porous microstructures under complex
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"Phase-space-inspired numerical methods for high-frequency wave scattering: from semiclassical analysis through numerical analysis to implementation". The design of fast and reliable algorithms
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decision is made Strong background in computational modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux
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cover will be constrained using Raman spectroscopy of carbonaceous material (RSCM method), complemented by bottom-hole temperatures (BHT) and heat flow data to define present-day thermal gradients
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Explainable AI on genomics data The candidate will: - Start by familiarizing with existing research and methods for genome interpretation - Familiarize with the sequencing data and its pre-processing - Study