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to test the current algorithm and inform development of future algorithm refinements aimed at supporting diabetic foot ulcer (DFU) prevention through identification of temperature differences of > 2.2°C
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departmental algorithms, the Scheduler critically evaluates the nature and urgency of scheduling requests to provide comprehensive clinical ambulatory scheduling for providers within the department specialty
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and Applications of Algorithms” at the Faculty of Computer Science. The position is limited to six months and is planned to be filled from 01.10.2025. Your future tasks: Participation in research
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Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention. Despite many studies introducing various APR techniques, much remains to be learned, however, about...
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Particle size distribution in flue gases for carbon capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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optimization (e.g. partitioning, distribution, indexing), and join algorithms (e.g. hash vs nested loop) · Experience in CI/CD automation for a data lake using CDK (Cloud Development Kit) or Terraform Preferred
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solutions for distribution power grid with renewable energy sources Development of simulation models and integration of CPS security algorithms into hardware-in-the-loop testbed environment and conduct
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working in the research group “Theory and Applications of Algorithms” at the Faculty of Computer Science. The position is limited to six months and is planned to be filled from 01.10.2025. Your future tasks
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a