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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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functionality. To explore the advanced materials, including MXene-based and other functional nanomaterials, for improved electrochemical performance. To investigate the smart, programmable electrodes that adapt
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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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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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manufacture, nuclear waste storage, green platforms for growth of functional materials, fuel cell hermetic seals, electrolytes, carbon capture solvents and thermal energy storage media. It is also vital
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, including both commercial tools and bespoke in-house apparatus. As a key member of our team, you will play a pivotal role in advancing the frontiers of drug discovery, laboratory automation, and the modelling
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to identify and promote approaches to reduce the environment impact of the sector. Ongoing research within Nottingham University Business School is examining the impact of this work within the social
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disassembly environment for recovery of critical raw materials, key to securing a circular supply chain to support a UK battery industry. As a PhD student, you will work with both academics from the AMT Group
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dynamics that govern flow boiling heat transfer and critical heat flux. The work ultimately contributes towards the development of improved methods for predicting critical heat flux in nuclear reactors
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functional performance of the components and the key process parameters. The project will deal with the design of special process setups, testing its working principles and performances followed by