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technological advances that support the global transition toward net-zero emissions and sustainable aerospace engineering. Motivation The reliability of electric propulsion systems is pivotal for next-generation
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make technological advances that support the global transition toward net-zero
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for the ‘Course Title’ using the programme code: 8856F Leave the ‘Research Area’ blank Select ‘PhD in Process Industries; Net Zero (PINZ’) as the programme of study You will then need to provide the following
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system
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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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-on experience in environmental management. Interdisciplinary training in hydrogeology, contaminant transport modeling, and decision-making. Networking & knowledge exchange with leading academic and industry
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of the Hub. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables
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(to help achieve net zero goals) can impact on the indoor environment and have positive or negative impacts on human health. For example, action improving insulation may increase thermal comfort within a
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labs framework. This studentship will include cohort-based training and activities, enabling students to gain wider skills and develop valuable personal and professional networks. Project Description