43 density-functional-theory-molecular-dynamics PhD positions at University of Nottingham
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. The objectives are to: Develop and validate a CFD tool for interface-resolved simulations of boiling, built upon existing software available at Nottingham. Use CFD simulations to characterise bubble dynamics and
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Dynamics (CFD). This is an exciting opportunity to contribute to cutting-edge research that supports the next generation of sustainable aeroengines. The successful candidate will join a supportive team of
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emissions. As a carbon-free fuel, it holds great promise for decarbonising energy-intensive sectors such as power generation and shipping. However, its practical use in combustion systems remains limited by
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record, in many areas of breast cancer including pathology; cell, molecular and radiation biology/radiotherapy; medical oncology/chemotherapy; translational oncology; clinical trials; pre-clinical models
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leverage advanced bespoke continuum robotic systems to demonstrate the feasibility of applying the proposed coatings can be deployed in-situ. Ultimately, this work bridges the gap between the theory
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aforementioned tasks with the following actions: Develop the principles and theories for governing the scalability principles for building innovative robotics end-effectors that can access geometrically complex
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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/or dynamic analysis of mechanical/robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the-loop
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analytical and numerical skills, with a well-rounded academic background. •Demonstrated ability to develop precision mechanical devices and mechatronics •Ability to develop kinematic and/or dynamic analysis
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difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation