43 computational-physics "https:" "https:" "https:" "https:" positions at University of Manchester
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of transformer insulation systems through laboratory experiments. This includes design and development of medium and large-scale experimental systems to replicate transformer chemical marker portioning process
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We are seeking a highly organised and proactive Programme Lead to play a central role in driving delivery, momentum and accountability across the People Directorate. This role sits at the heart
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for you and your family; Well-being programme with counselling, fitness and leading sports facilities; Learning and development opportunities; Season ticket loans for public transport; Workplace nursery
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your application now you acknowledge that you are aware that such screening will take place, and agree to take part in the process. What you will get in return: Fantastic market leading Pension scheme
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About the role: This is a research project taken in the Digital Manufacturing Lab led by Prof. Charlie Wang. The project aims to a computational kernel to effectively generate optimised structure
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of the governing physics. One example is to assume that flow is incompressible. The more accurate approach is to capture the volumetric dilation during vapourisation/condensation as a metallic substrate vapourises
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Responsibilities, Accountabilities and Duties: Undertake qualitative interviews Process and manage interview data Code and analyse qualitative interviews Maintain accurate and complete records of all research data
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climate change. This process can be achieved using an electrochemical system. The successful candidate would be expected to work with the Research Associate in Electrochemistry in the team and provide
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, systems, process flow, governance and BAU service design standards. Integration is core to most projects IT Services undertake at the university, and assignment to a range of ongoing projects will form a
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and materials engineering. They will do this by integrating modern data-centric approaches, such as physics-informed machine learning, structure-aware modelling, and digital-twin methodologies, with