336 programming-"https:"-"FEMTO-ST"-"UCL"-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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Fluid Dynamics. Excellent programming skills in C, Fortran, or any other high-level language. Desirable: final-year project on a fluid mechanics problem. Other requirements: Unique self-motivation and
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skills, including MS Office and other programs e.g. photoshop High-resolution confocal imaging experience Basic programming skills Excellent knowledge of Drosophila genetics Very good ability to explain
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algorithms and machine learning. Strong background in programming (e.g. MATLAB, Python) and tools (e.g. Ansys, HFSS or CST Studio Suite). Good Hands-on skills using equipment and hardware platforms including
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, finite difference, and deep AI optimisation. You will receive training in all of these and various programming languages throughout the PhD. MetaHUB is a multi‑institution research collective, with
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discovery programs using visible light-mediated catalysis. We are particularly focused on leveraging biomimetic pathways for the homologation of amines. This is an industry-funded organic chemistry project
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students from all nationalities. They support a full stipend at UKRI rates (currently £20,780 per year) and tuition fees for international/home candidates for the duration of the programme. How to Apply All
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, you will acquire a unique and valuable set of transferrable skills ranging from designing complex sample environments and experimental protocols, to programming and data mining, effective communication
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International applicants. Funding Details: Starts September 2026 4-year award Funding covers full tuition fees plus UKRI stipend (£20,780 in 2025/26) How to apply: To apply, click “Apply for a Programme” (via the
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programming and/or software engineering Electrical engineering and/or power systems. Application Procedure Informal enquiries are encouraged and should be addressed to Dr Jack Umenberger (jack.umenberger
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programming and software skills (Python essential; C++/Fortran beneficial) Interest in machine-learning-based regression and surrogate modelling Motivation to challenge established CFD-driven design workflows