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aims to develop a novel theoretical framework for nonlinear and robust control of dynamical systems from a phase perspective. You will have the opportunity to freely explore multiple research directions
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to develop novel, bio-inspired neural networks that flexibly and robustly control locomotion in multi-limbed robots. "Self-organised clocks for reliable spiking computation" (Supervisor: Prof Timothy O'Leary
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vulnerable parts of the system for attacks, model faults and attack risks, and develop new control architectures that mitigates them towards achieving operational resilience throughout their life. Your PhD
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complex metal structures. This opportunity is centred around improving manufacturing productivity with advanced laser-matter interactions control and optimisation. The PhD will advance our comprehension
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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will also be part of the Neural Control Project, which is a collaboration between Maastricht University and Radboud University. You will work closely with leading researchers in machine learning
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This PhD project will develop next-generation grid-scale energy storage solutions integrated into HVDC (High Voltage Direct Current) systems at the University of Edinburgh, in partnership with UK
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Organisation Job description Project and job description This PhD position is dedicated to advancing autonomous robotic manipulation and control within a textile-sorting cell, where garments arrive
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are in Bristol. Combined, the ability to sensitively evaluate a humans perception of thermal comfort under well controlled environments offers an opportunity to develop protocols that seek to evaluate
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controlled via structural phase transitions or external fields. The successful candidate will develop and apply a range of theoretical and computational methods based on first-principles electronic structure