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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest
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Generative Models" (UCL , Oxford, Imperial, Edinburgh, Cardiff, Manchester and Surrey) and with its industrial partners. Key responsibilities include working on deep learning, probabilistic modelling, deep
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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impact, leveraging one of the highest-quality financial datasets in the industry. What You’ll Do Conduct research and develop ML models to enhance trading strategies, with a focus on deep learning and
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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale