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, autonomous learning agents are likely to take an active role in human society, engaging in daily interaction and collaboration with humans. Developing learning algorithms that enable these agents to produce
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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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. 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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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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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