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. Without these guarantees, the algorithms will remain limited to experimental testbeds. The aim of this project is to address this limitation by combining the complexity of deep-learning control policies
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at the University of Sheffield. Ultra-thin membranes are key to many modern technologies — from flexible solar panels and sensors to clean-energy catalysts. Making these membranes reliably is difficult, and this
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also be joining the Leonardo Centre for Tribology, which is an active and friendly group. There are ~25 PhD students working on machine elements, tribology, lubrication, and sensor systems for wind, auto
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genetically encoded calcium sensors (e.g. GCAMP) to study how visual pathways operate in synchrony. In zebrafish, you will apply neurotransmitter sensors ("glutamate sniffers") to map glutamatergic
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property distributions from process-induced microstructure variations using the ML models created. Work with the extended team to link the simulation of sensor data with new multi-scale processes
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haptic guidance methods that respond to operator skill levels. Identify trajectory features that characterise expert performance for training robots. Develop algorithms that allow robots to refine
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. Optimal sensor placement, identified through adjoint-based sensitivity analysis to improve assimilation efficiency. By embedding physical laws into data assimilation, these methods bridge the gap between
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-identified scans, records and sensor feeds to answer questions such as: Can we predict a patient’s response to treatment without ever seeing their raw file? Can an algorithm learn the warning signs of trouble
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with UAVs for tackling wildfires, including modular, scalable methods with respect to the volume of collected data, to the number of UAVs, data sampling rates, number of sensors, centralised
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extraction? What would be the best choice of solvent? What is the optimal route to recycle the water in the fermentation broth? Answering these questions requires us to develop new design algorithms. It is