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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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et al (2015). A hybrid metaheuristic algorithm for the multi-depot covering tour vehicle routing problem. European Journal of Operational Research.
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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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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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
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2025 Reference: RD-PHD-01-LS-MH-25 Project Title: The cost of silage production: measuring the carbon footprint of silage Primary supervisor: Prof. Liam Sinclair Co supervisors: Dr Kate Le Cocq, Dr
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2025 Reference: RD-PHD-02-LS-MH-25 Project Title: Dietary strategies to reduce methane production in dairy cows and their effects on the rumen microbiome and metabolism Primary supervisor: Prof. Liam
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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). Supervisors Primary supervisor: Prof. G.Tasca. Co-supervisors: Prof. J.Diaz-Manera, Dr. S.Cockell. Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree or international