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are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a
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(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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multiple departments within the University of Cambridge as well as the collaborating organisations (RSBP, NIAB and UKCEH). The role holder will investigate machine-learning approaches that advance the core
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that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD project will: Investigate the multi-stage modelling
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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Science, Machine Learning, Finance, FinTech, Economics, or a related field. Candidates should demonstrate knowledge of Large Language Models, generative AI, and machine learning, with interest in financial