134 programming-"https:"-"https:"-"https:"-"https:"-"https:"-"Department-" PhD positions in United Kingdom
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for steel metallurgy, electromagnetic sensing, and high-temperature experimentation. It will run alongside the major UK research programme Frontiers in Electromagnetic Non-Destructive Evaluation Research
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Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD
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project offers an opportunity to explore potential novel therapeutic ways of activating ferroptosis against cancer. Ferroptosis, a recently discovered iron dependent non-apoptotic form of programmed cell
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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Science, Applied Mathematics or a closely related field. A strong mathematical background (probability, linear algebra, optimisation) and proficiency in programming (preferably Python/Matlab) are essential. Prior
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Sustainability (RAPTOR). The successful candidate will be part of a cohort of PhD students across four universities (Manchester, Liverpool, Surrey, Suffolk) working in a national programme with 18 industrial
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programme with 18 industrial partners. During the first months of this PhD, training will be provided UK experts in radiation protection, environmental assessment and radioactive waste management, nuclear
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in programming for data analysis and algorithm development (e.g. Python, MATLAB). Strong analytical skills and the ability to critically evaluate technical and scientific literature. Excellent
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following would be advantageous: Programming in Python, R or a comparable language. Using machine-learning frameworks such as PyTorch, TensorFlow or scikit-learn. Working with audio, video, behavioural
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start date Programming skills in Python, R or STATA Willingness to learn unfamiliar tools as needed (e.g. GitHub, APIs, new libraries). Familiarity with econometric techniques, especially panel data