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Field
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language understanding (to interpret instructions), and action generation (to respond), enabling robots to perceive, reason and act flexibly. The models will be trained on simulated datasets to learn general behaviours
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events
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avenues by enabling chronic, gut-based monitoring of neuroendocrine activity for applications such as closed loop therapeutics. The proposed PhD project sits at the interface of biomedical engineering
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control strategies integrating fuel, engine, electric machine, and energy recovery systems for improved overall efficiency. Validate the developed methods through experimental and simulation studies
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(e.g., wind-turbine blades, rails, laminates). Building on our recent “FNO-Kernel” work—embedding a physics-based convolutional kernel inside the Fourier operator—the PhD will deliver operator-learning
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quantum mechanical effects are typically too expensive for simulations of disordered systems like liquids. This PhD will develop and deploy the tools needed high-fidelity simulations: machine learned
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The construction industry accounts for 8–10% of global anthropogenic CO₂ emissions and faces significant challenges in achieving NetZero targets by 2050. This PhD project offers an exciting
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models