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Field
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profile. The successful candidate will primarily advance data analysis, algorithm development, modeling, and translation of complex measurements into practical decision-support, process-optimization, and
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will also investigate process optimisation algorithms, providing a route toward autonomous optimisation and ultimately enabling long term stable operation under variable load conditions. This post is
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-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs. The successful
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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, quantum science, condensed matter, materials, photonics and interdisciplinary applications of physical science. This role sits within a collaborative quantum science project at King’s on quantum algorithms
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. The tasks include battery cell characterization and modelling based on laboratory tests, and development of algorithms for estimating the charge level, health, and power capability which includes robustness
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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developing algorithms for robot navigation, path and trajectory planning, resource allocation, coordination, or distributed decision-making. Knowledge of wireless communication systems, communication-aware