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the groups of Dr Joe Forth, Dr Anthony Bradley, and Project Lead Professor Steve Rannard, applying your expertise in machine learning, cheminformatics, and soft materials to accelerate LAT design and
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asynchronous AI-led chemical optimisation across chemistry laboratories¿. This role sits at the intersection of robotics, machine learning, and chemistry, aiming to develop robotic systems that work
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of machine learning-based automated ultrasound video analysis models that incorporate temporal reasoning. The research will also include work that aims to understand how human behaviour may change with
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of probability of statistical machine learning. They will possess sufficient specialist knowledge in network analysis and uncertainty quantification in machine learning and have the ability to manage
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near completion) and have publications in rejection learning or learning-to-defer techniques. You should also have experience of original machine learning architecture design and ideally have prior
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the area of probability of statistical machine learning. They will possess sufficient specialist knowledge in network analysis and uncertainty quantification in machine learning and have the ability
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responsible for the design and testing of original machine-learning based methods for fetal heart biomarker discovery from the CAIFE image and video dataset. The full-time post is funded by InnoHK and is fixed
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predictive control of carbon mineralisation through high-throughput mineralogy and machine learning.” This is an exciting opportunity to contribute to innovative research at the interface of mineralogy
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as
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Applications are invited for a Postdoctoral Research Associate in Machine Learning for Chemistry to work in the research group of Professor Volker Deringer at the Department of Chemistry. About the