196 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NOVA.id" positions at University of Nottingham
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, including bespoke courses for Engineering researchers on academic writing, networking, and career development. The faculty also offers outstanding facilities and maintains strong partnerships with leading
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, inclusion, and the values of public service. What We Offer You will join an ambitious institution at an exciting point in its development, with the opportunity to shape governance at strategic and operational
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week, a five-week professional development/elective period, and a five-week senior assistantship (SAST). You will be the AP2 Lead for the current curriculum and become responsible for overseeing Year 5
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interpersonal skills, a collaborative approach to working within multidisciplinary teams, and enthusiasm for supporting the learning and development of final-year veterinary students. You should be comfortable
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and training from both academic and industrial researchers, and gaining direct exposure to industrial CFD workflows and software development practices. Candidate Requirements We are seeking
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supportive working environment • We are committed to staff development through the provision of training, continued support, and career progression opportunities Further information is available in the role
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will contribute towards the ongoing development and implementation of effective procedures and systems supporting research contracts and interfaces with other teams. This is a permanent post, hours of
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support for your ongoing professional development. These roles are offered on a part-time basis with a minimum of 0.3 FTE and a maximum of 0.8 FTE. The total FTE available is 3.5 but the specific details
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, 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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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe