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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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as soon as possible but must be available to start by 1 April 2026 at the latest. This project aims to develop superconducting microwave interconnects and metasurfaces for distributed quantum networks
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engineering, linked data, web technologies. About the role: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out
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RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment
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: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out research in neuro-symbolic AI, with a focus on using generative and
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, data-limited, and dynamically evolving environments. Applications include complex engineered systems such as intelligent communication networks, distributed computing platforms, and quantum-enhanced
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limited to: Implement, optimize, and evaluate efficient algorithms and software tools applied to reference genome assembly and curation, and to the analysis of genomic data broadly Contribute
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep