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- UNIVERSITY OF VIENNA
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address: Dr Fahad Panolan: f.panolan@leeds.ac.uk Project summary The Algorithms group at the University of Leeds (UK) is offering a fully funded 3.5-year PhD studentship on Parameterized Complexity and
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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Kyropoulou and will work in areas related to (Theoretical) Computer Science, Algorithmic Game Theory, and/or Fair Division with potential applications in Blockchain in the context of the EPSRC funded research
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the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year. Modernised distribution power networks face an unprecedented challenge as thousands of power
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freshwater fishes, structured around the following objectives: Use the LOC to map the freshwater fish distributions in Madagascar, including threatened, invasive and human food species Create predictive models
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PhD Studentship: Open Radio Access Network (ORAN) for Distributed Edge Computing Orchestration in 6G
://cheddarhub.org The work is envisioned to have great impact on design and development of intelligent AI/ML orchestration algorithms in real 6G experimentation test beds. The applicant is envisioned to further
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://cheddarhub.org The work is envisioned to have great impact on design and development of intelligent AI/ML orchestration algorithms in real 6G experimentation test beds. The applicant is envisioned to further
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PhD Studentship: Accelerating Statistical Algorithms through Machine Learning Award Summary 100% home fees covered, and a minimum tax-free annual living allowance of £19,237 (2024/25 UKRI rate
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. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
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optimisation algorithms to dynamically reconfigure the substation/distribution network settings to enhance the system efficiency. The optimisation algorithms will incorporate the uncertainties associated with