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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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become available, it’s not clear whether these tests are sufficient, and what the most effective method to achieve cervical spine motion control is. The proposed project will simulate various rescue
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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fine-scale, fully distributed hydrological modelling, with the ultimate goal of optimising NFM strategies in moorland, to improve flood resilience for rural, upland communities. The studentship will be
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engage in immersive, simulated construction tasks, while wearable sensors monitor their physical effort, emotional states, and cognitive load. Physiological and behavioural data — including eye tracking
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technology review to understand this technology and its recent development for electrical engineering application. Real-time simulation platform skills development including Typhoon and SpeedGoat. Development
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will contribute to the enhancement and implementation of a digital model representing the Luxembourgish and cross-border energy landscape. The doctoral student will be a member of the Doctoral School
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minimum of one year at second-cycle level. Applicants must submit: a CV a two-page proposed research statement copies of degrees and transcripts of all academic records copy of the MS thesis at least two
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-time digital twin technology review to understand this technology and its recent development for electrical engineering application. Real-time simulation platform skills development including Typhoon and
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/emissions modelling preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional