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application via the University of Bristol’s online portal. Select “Management (PhD)” on the programme selection page. Candidate requirements: A first-class honours degree (or international equivalent) or strong
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have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge of interdisciplinary
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Chemistry, Physics, Chemical Engineering or Material Science. Solid knowledge in Physical Chemistry and Surface Science. Pre-knowledge on programming and Molecular Simulations (MD or MC) are desirable. Only
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located in Central Cambridge, Cambridgeshire, UK. The key research and scholarship responsibilities and duties of this role are: Plan and manage research activities. Support an investigation into carbon
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vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable
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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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or environmental subject or a relevant Masters qualification. If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with
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general enquiries on the scholarships, please email: Research Programme Leader, Dr Irma Malafronte, at irma.malafronte@roehampton.ac.uk For administrative support regarding the application process, please
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part of the EPSRC Centre for Doctoral Training (CDT) in Net Zero Aviation, offering an integrated, multidisciplinary training programme focused on innovation, collaboration, and inclusive leadership. As
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existing experience. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based training programme with emphasis on innovation and impact, collaborative working and learning