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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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: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum‑classical algorithms to tackle domain‑specific, computationally demanding problems. Methods
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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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characterising chronic diseases and disease patterns from electronic health record (EHR) data through the development of advanced deep learning methodologies based on state-of-the-art foundation models. You will
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) as part of the ACORN IAA and is for 12 months. The researcher will develop visual mapping and change detection algorithms
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for cardiometabolic disease with associated neurodegenerative conditions. It aims to develop novel deep learning algorithms, audio and vision transformers, and hybrid attention mechanisms, to detect
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) until 31 March 2027 (with the possibility of an extension). The appointed candidate will support advanced research initiatives focusing on systems design, distributed systems, and algorithmic optimisation
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research and implementation. You will also develop novel algorithms using state-of-the-art computer vision and machine learning techniques (segmentation, multimodal AI, foundation models, agentic frameworks
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, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming, analysing and interpreting data, and contributing to innovative solutions that support maritime
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codes and process their results. Helping to develop new models and algorithms to simulate pulse propagation, the material response, and other aspects of our experiments. Coding in Julia and python. Take a