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Field
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computing. This will include, but is not limited to, the design of distributed quantum algorithms, circuits, and error correction, as well as the interplay between circuit optimization and circuit
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technologies (LCTs, such as solar PV, electric vehicles, heat pumps or energy storage) to distribution networks. New active technologies that provide flexibility, such as network operator-owned power electronic
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. Applicants with a strong background in optimization (including machine learning), algorithmic game theory, decision theory, computational social choice, social welfare, or fairness optimization are welcome
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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Computer Science• Distributed systems (Cloud, Edge)• Scheduling and optimization algorithms• Data streams, data management and data pipelines• software engineeringExpert programmer• Java• at least one other
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Your profileYou are enthusiastic about developing and investigating innovative research ideas and systems within the discipline of distributed systems? You have a master’s or equivalent degree in
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. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also expected
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. The Role This is an exciting opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions
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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also
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, CU/DU/RU split). - AI/ML algorithms and methods for joint compute and radio resource management at the 6G edge leveraging accelerators - Control- and data-plane separation, real-time scheduling, and