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network attractors, funded by The Leverhulme Trust. This is a brain inspired project in the field of Neurodynamics. Networks of oscillators are ideal candidates for modelling patterns of functional
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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emissions from transport. Decarbonising aviation is a vital part of achieving net zero. Hybrid and ‘all electric’ aircraft technologies offer a pathway to net zero. The electrification of aircraft, for both
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learning from sensor feedback. This PhD aims to develop novel algorithms for Artificial Intelligence (AI) driven continual learning via reinforcement learning (RL), incorporating mechanistic knowledge
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robots. We currently imagine these to be soft materials that dress both human and robot bodies. Embedded sensors will capture data about mutual touch. Embedded actuators will deliver haptic feedback
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Bragg sensors. Demonstration for evaluating the developed snake robot and its navigation and localisation strategies. We are seeking talented candidates with: First or upper second-class degree in
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the paper industry. Motivation This project will form part of a wider network of European partners spread across Europe from Universities (Nottingham/Ghent/Maastricht), smaller SMEs (Nova Biochem), Research
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Background Network Rail operates several telecom networks which provide connectivity for various signalling systems. Therefore, the performance of telecoms assets is integral to how the railway system operates
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mobility demands motors with higher efficiency, reduced lifecycle impact, and compatibility with evolving drivetrain architectures, there is a clear need for new machine concepts that align with Net Zero
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Net Zero 2050 goals, electric motors must undergo a transformational leap—from today’s typical power densities of 2–5 kW/kg to a step-change 10–25 kW/kg by 2035. The highest power dense motors today