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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM
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are these impacted by different land use scenarios? How can we use machine learning to predict soil microbiomes from either pristine or agriculturally used soils, predict their functional capacity (e.g. carbon storage
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for the challenging tasks facing biomedicine and public health. We are seeking a highly motivated Research Associate in Biomedical Machine Learning, who is passionate about advancing machine learning methodologies
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of the art 20,000m² Diamond Building, you’ll be inspired by what we can offer our Engineering students. We have world leading facilities in teaching and learning, with 15 specialist laboratories designed
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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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climate scientists and artificial intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine
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to co-develop tools that are embedded into routine health systems and data dashboards used to guide policy decisions. This role sits at the intersection of generative machine learning, statistical
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of multimodal imaging, including spatial transcriptomics, single cell and large-scale omics data, digital pathology, using cutting-edge AI and machine learning approaches. You will play a critical role in