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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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-phonon interactions, which together form tri-partite coupling that gives rise to effective optomechanical interaction between collective excitonic states (optical) and vibrational modes (mechanical
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areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
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This project focuses on reducing aerofoil broadband noise, specifically turbulence–leading edge interaction noise and trailing edge self-noise, commonly encountered in aero-engines, wind turbines
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to work and interact with other members of Division C in the Engineering Department as well as our industrial partners. They will contribute to seminars and to take part in the research group's broader
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broader field of biophysics that will contribute to the interactive lab culture. We look for friendly and driven colleagues to enrich our team. UK visa costs and NHS surcharge fees will be covered. We would
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and objectives. • To deal with problems that may affect the achievement of research objectives and deadlines by discussing with the Principal Investigator or Grant-holder and offering creative
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have experience in one or more of these subject areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. What you would be doing: You will be creating data
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interaction and origins of mechanical failure under pressure. They should have expertise in microelectrode arrays and multilevel high-density routing for large-area sensor systems. Experience in multicomponent
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lines into endothelial cells and OPCs, as well as conducting co-culture experiments to assess APOE4-related cellular interactions. It requires the use of molecular and cellular biology techniques