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A fully-funded 3.5-year PhD studentship (covering UK home student fees and an annual stipend of £21,805) is available in Applied Mathematics for Formulated Products. The PhD will be under
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This PhD project will develop mathematical models to investigate population dynamics in biological systems. Combining dynamical systems theory, mathematical modelling, and data-driven approaches
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A PhD studentship is available for commencement October 2026 – January 2027 as part of the first Cohort of a new EPSRC Centre for Doctoral Training in UK Semiconductor Industry Future Skills* led by Swansea University in partnership with the University of Leeds. The PhD studentship will be: 4...
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Application deadline: 30.09.26 Research theme: Applied Mathematics, Thermal-Fluid-Dynamics, Multi-Physics How to apply: https://uom.link/pgr-apply-2425 UK only This 4 year PhD project is fully
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About the project: Multiscale Molecular Dynamics: a mathematical exploration of coarse-graining Supervisor: Dr Thomas Hudson, University of Warwick When applied to molecular dynamics simulations
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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computing? Do you enjoy studying complex quantum mechanical systems both from a physical and a mathematical perspective? Are you an innovative and productive team player who enjoys being part of
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or Mathematics. Excellent English written and spoken communication skills. It is desirable that candidates possess expertise in some (but not all, or even most) of the following areas: Microbiology, Bacteriology
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investigate how these processes might be represented in an innovative mathematical manner, without assuming that the ecosystem structure itself remains fixed. Thus, it will delve into the question as to how we
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new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as