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their home countries and beyond. Training and Career Development As a PhD student, you will join the Faculty of Science Graduate School, which provides a comprehensive programme of professional skills training
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their practical deployment. The Project: This PhD will develop the science and engineering required to overcome these bottlenecks, with the following objectives: • Uncover the mechanisms driving enhanced hydrogen
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not sufficiently understood. This involves complex physics at the interface of plasma physics, shock physics, material science and thermodynamics. The objective of this project is the computational
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 11 days ago
. The research will be computational based, and at this stage is still broad, so we can formulate the optimal plan for the right candidate. We will take an interdisciplinary approach, and you will be able
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includes projects in biomedical instrumentation, experimental hardware automation and programming, and computational science. Scientific publications in a relevant area are considered a significant benefit
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Engineering, Mechanical Engineering, Aeronautical Engineering, Automotive Engineering or other relevant Engineering and Science subjects, or relevant industrial experience. English language requirements
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production. Candidate Profile: We are seeking an ambitious and highly motivated candidate with a strong background in applied and quantitative ecology, environmental science, biology, or a related field. The
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The PhD research project is an EPRSC Industrial Cooperative Awards in Science and Engineering (ICASE) with Constellium, a global leader in the development, manufacturing, and recycling of aluminium
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initially, with possibility of extension. Although the post is particularly well-suited to candidates who ultimately plan to pursue a PhD, we ask applicants to commit to at least 12 months in the role so that
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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning