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evaluating trade-offs under different environmental conditions, such as changing weather patterns, fluctuating energy costs, or variations in occupants’ schedules. Typically, the initial priority settings
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): Network-level design and multi-node cooperation (coordination, topology design, distributed/federated learning, etc.) Wireless resource allocation and scheduling under multi-objective KPIs (rate, latency
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possibility for further extension dependent on funding. Initial interviews have been scheduled for September 2025. For informal enquiries, please contact, Prof Gerasimos Theotokatos, MSRC Director at
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limited to, age, race, disability, ethnicity, gender, gender identity, geography, sexuality, and social class. Interviews for shortlisted candidates are provisionally scheduled for Tuesday 11th November
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priority areas. These projects are all scheduled to commence in February 2026. Successful applicants will benefit from supervision by experienced and research-active colleagues, access to dedicated doctoral
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learning applications to improve project cost, risk, and schedule performance Please state your entry requirements plus any necessary or desired background A first class or an upper second class British
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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) at the latest. Applicants selected for an interview will be notified by September 5, 2025. We intend to schedule interviews (in person or via Zoom) in the second week of September. The ideal start date would be
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on schedule, examples of overcoming barriers to progress etc) Teamwork: Demonstrable experience of working in a collaborative manner with team colleagues at all levels, as part of a diverse, inter-disciplinary