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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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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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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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, and edge computing, AASs are evolving into smart, interconnected solutions for addressing the dynamic challenges of modern cities. This project investigates how to coordinate these multifunctional
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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present their data pipeline is overburdened taking too much computational time and requiring the use of expensive cloud resources. In this project we will use NAS approaches to reduce the computational burden and
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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). The Computer Science group is looking for students to work on one of the following projects Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures Carbon-Conscious Resource Scheduling for AI Workloads
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engineering, or atmospheric science* Expertise in and passion for computational modelling and software development/engineering Expertise in cloud physics or contrails preferred but not required Creative problem
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system stems from the need to increase efficiency in marine monitoring. Furthermore, existing computer vision solutions often depend on cloud computing infrastructure and require specialized expertise. A