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Field
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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by the National Lottery Heritage Fund. As part of this studentship, you will learn crucial collections management and public engagement skills to inform the project and your work, and will play a key
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our team but also domestically and internationally. A successful candidate will be open to learning new research areas in the collaborations. With a high research performance, there are potential
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-workers working on related projects • Be willing to learn new techniques and apply them in an interdisciplinary research environment in an autonomous manner • Be able to work as part of a team and
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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structure would enable you to understand science better at atomic level. You will learn the skills of presenting the results to small and large groups of people via presentations in conferences and meetings
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science, mechanical engineering or physics. This interdisciplinary project requires a passion for both experimental work and computational modelling, along with a keen interest in learning fracture
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training programme with emphasis on innovation and impact, collaborative working and learning, continuous development, active engagement with partners and stakeholders and inclusion of student-led activities
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) in a relevant subject (Physics, Chemistry, Materials Science, Chemical Engineering), experimental track record and willingness to learn. Home rate fees are fully funded. Applicants from overseas will
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opportunity! For more information, please contact Prof. Rob Dwyer-Joyce at r.dwyer-joyce@sheffield.ac.uk Learn More: Leonardo Centre: www.sheffield.ac.uk/leonardocentre John Crane Ltd: www.johncrane.com Become