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. This information is not known a priori, so it has to be evaluated in an accurate way. Currently, there are two main techniques for this purpose, the recycling (precursor) methods and synthetic methods. The first
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Modelling Neutrino Interactions for Next Generation Neutrino Experiments School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Patrick Stowell Application Deadline: Applications accepted all year round Details Neutrinos play a crucial role in our understanding of the...
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Simulation of particle physics experiments on graphical processing units School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof D Costanzo Application Deadline: Applications accepted all year round Details In this project you will combine state of the art software...
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. Modelling investigations are required to improve methods that apply turbulent fluctuations in the velocity field to the particle motion, as well as methods to resolve gas species concentrations
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programme grant on hybrid energy storage systems for grid-independent EV charging stations and there will be significant opportunities for the successful candidate to interact with the interdisciplinary team
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. The system will incorporate the expert suites for risk assessment techniques and frameworks, as well as the codes/standards and numerical models to predict the consequences of fires, explosions and other
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will work with the programme leader, academics and practice partners to support students and will attend meetings across the partnership locations. They will be expected to contribute towards development
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. withdrawals, change of programme, leave of absence). Assisting with other activities associated with the change of status process, e.g. reporting to the Student Loans Company, sending standard confirmation
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AMRC marketing teams Programme Administration & Support Be the point of contact for students and supervisors, and provide guidance and support on matters related to the PhD and EngD programmes Support
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Early-stage failure prediction in fusion materials using machine learning