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production. Candidate Profile: We are seeking an ambitious and highly motivated candidate with a strong background in applied and quantitative ecology, environmental science, biology, or a related field. The
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/ statistics / theoretical physics / computer science background to whom the project outline appeals are encouraged to directly reach out to Dr. Yihan Zhang. Full details on how to apply can be found here
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animals, while Prof Durbin's works on computational genomics and large scale genome science, including the development of new algorithms and statistical methods to study genome evolution. Moving forward
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), or international equivalent, in Engineering, Computer Science, Physics or Mathematics with evidence of programming experience. Equality, diversity and inclusion is fundamental to the success of RAINZ CDT and is at
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: You should hold a first degree equivalent to at least a UK upper second class honours degree, or suitable postgraduate degree in computer science, human factors, psychology, social science or a health
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there be interest, there is also the possibility of developing teaching and supervision skills on our MSc Astronautics and Space Engineering programme. Sponsored by EPSRC and Cranfield University, this DLA
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of Electrical Engineering, School of Electrical, Electronic and Mechanical Engineering, University of Bristol This project is funded as part of the REWIRE IKC. The studentship provides funding for tuition fees
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quantitative methods, prevention science and intervention studies. The postholder is expected to work collaboratively with other programme members spanning multiple academic and professional practice disciplines
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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sweat distribution across impairment groups which may inform future clothing design for improved thermoregulation. Additionally, it will explore cooling interventions, using computational modelling