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Field
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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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Predictive Control (MPC) algorithms, innovative coalition-formation techniques, and validate these through high-fidelity simulations. You will design, implement and validate innovative data-driven economic
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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operating filters. Quantify operational performance including headloss recovery, filtrate turbidity, biological stability and lifecycle carbon—using high-resolution sensor data and life-cycle assessment tools
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, biomechanical aspects related to wearable sensing, and optimisation of sensors and wearable electronics for selected healthcare and sports applications. Your Group This project is part of the prestigious
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scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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experience with maritime systems, radar/sensor fusion, or industry engagement is desirable. What we offer For information about our rewards and benefits please visit https://www.ucl.ac.uk/work-at-ucl/reward