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comparing different methods. The successful candidate will be jointly supervised by: Dr Edward Gillman (www.nottingham.ac.uk/physics/people/edward.gillman ) and Professor Juan P. Garrahan
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"Confirmation of funding" must be attached/uploaded during the application process. Candidates should supply a brief research proposal of 2-4 pages. This should include a brief literature review to the research
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(matti.kuittinen@aalto.fi ). For questions related to the application process, please contact HR Partner Enni Ailoranta hr-arch@aalto.fi . We will go through applications, and we may invite suitable candidates
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physical and numerical modelling. Feel free to reach out to the project supervisors if you have any questions. Entry requirements: The ideal applicant will be enthusiastic and self-motivated with a first
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• Discounted bus travel and Season Ticket travel loans • Membership to a variety of social and sports clubs This role also offers the opportunity for hybrid working. Application Process You will be
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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, dynamical systems and statistical physics. The candidate will be jointly supervised by the Coventry team Dr Fei He and the Stellenbosch team Prof. Francesco Petruccione . This project will contribute
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propellant space propulsion systems. A significant limiting factor of hybrid propulsion systems is the continuous change in surface area of the propellant grain during the combustion process. This changing O/F
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Ascl1 are important. We have undertaken a comprehensive discovery experiment to identify all the proteins that can physically interact with Ascl1, using a method we developed called RIME (Rapid
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive