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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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(particularly under extreme conditions), and/or the use of machine learning for solid mechanics/stress analysis problems are encouraged to apply. The job description presented here is deliberately broad due
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developing ideas for application of research outcomes. This post also be linked to research activities linked to the Faculty’s research platforms such as the Power Electronics, Machines and Control Research
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research in areas including Artificial Intelligence, Big Data and Visual Analytics, Computational Intelligence, Machine Learning, Software Implementation and Testing, and their applications in manufacturing
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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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engineering disciplines. There will be opportunities for you over the apprenticeship period to learn and train on a wide range of cutting-edge engineering technologies and disciplines including: CNC machining
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(Sommerschield et al. 2023 ‘Machine Learning for ancient languages: A survey’, Computational Linguistics, 49.3) mean that it is time to test the latest digital tools (e.g. GPT-4o) to undertake four main tasks