70 phd-computing-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at Cranfield University in United Kingdom
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This fully funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA) and RES Group, offers a bursary of £25,000 per annum, covering full tuition fees. The project focuses
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the growing demand for sustainable AI-enabled systems, this PhD brings together low-power computing, energy-aware design, and thermal optimisation. You’ll work with advanced profiling tools, prototype long-life
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This exciting fully funded PhD, with an enhanced stipend of £25,726 per annum (with fees covered), will deliver a comprehensive understanding of micropollutant removal in different types of nature
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members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK
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their full potential. We are a Disability Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and
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Design and Manufacturing Engineering to Tackle Global Sanitation Challenges - MSc by Research or PhD
of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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at Cranfield which has a strong collaborative history with industry in the field of atmospheric icing science research. This programme provides the PhD candidate with an outstanding opportunity to work across a
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM