11 postdoc-in-postdoc-in-automation-and-control Fellowship positions at University of Nottingham
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individual and collaborative research in the area of Power Electronic, Machine and Control. The role holder will be expected to conduct and lead high-caliber, impactful research at the forefront of Power
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(hiPSCs), cardiomyocytes, metabolism About the project We are recruiting a postdoc (established or newly graduated from their PhD) who has an exceptional ‘can-do’ attitude, with drive and enthusiasm to push
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fidelity software-based simulation tools to advance the design, optimisation and manufacture of magnetic field shielding and control systems for a diverse range of applications. These field control
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developed in our labs show great promise in reducing infections and controlling host response in the clinic. We recently added the use of micro topography to provide a synergistic stimulus to tackle
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initiatives Travel opportunities to visit international partners and attend conferences What you should have: A good first degree (BEng/BSc with 1st or 2.1) in Robotics, Mechatronics, Control, Mechanical
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, alignment, and characterisation of laser-based instrumentation. • Strong experience with computer programming, both for signal processing and experimental hardware control (e.g., C, C++, Python, MATLAB
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of technologies/products. • Proven hands-on experimental skills of developing hardware. • Practical experience with optical fibres. • Proficiency in writing instrument control and data analysis software
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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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code for experiment control, data acquisition, and data analysis. They will be expected to work effectively with Dr. Smith, collaborators, other group members and contribute to the dissemination
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position that can be applied across a broad range of industrial sectors, bringing the benefits of passive linear optical superresolution to the domain of in-process control for additive manufacturing