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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
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management, computer science, information management and beyond. Supervisors Primary supervisor: Professor Tom Jackson Secondary supervisor: Professor Ian Hodgkinson Entry requirements At least a 2:1 honours
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Natural Language Processing (NLP) in the areas of culturally aware NLP or multilingual conversational NLP, and integration of such methods to support language technology in multiple languages
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an interest in data science, computational chemistry or related areas. A degree in Chemical Engineering, Chemistry, Computer Science, Mathematics or other engineering/science disciplines with significant
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Engineering, Physics or Materials Science Excellent English written and spoken communication skills The following skills are also highly desirable: Ability to program in Matlab, Python or similar Strong
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engineering. Applicants are expected to have (or be close to obtaining) a PhD in information engineering, computer science, civil engineering or cognate discipline. They should also have good written and spoken
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from the Defence Science and Technology Laboratory (Dstl). Award value The Studentship covers: Stipend: at UKRI rate + London Weighting (£22,780 for 2025/26 academic year) Tuition fees: UK Home Fees for
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers
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EPSRC iCASE PhD studentship with SLB - Computational modelling of advanced geothermal systems School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded UK Students
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | about 1 month ago
, conducted in collaboration with BASF and Heriot-Watt University. The initial study utilised advanced flow chemistry techniques to accelerate the biodegradation of BASF-supplied polymers, employing size