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Field
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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pathology on computational, molecular, cellular, preclinical and translational levels. A spectrum of scientific methods includes state-of-the-art multi-omics approaches, machine learning and implementation
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with epilepsy across multiple NHS hospitals. They are expected to have some experience working with NLP in general and LLMs in particular. They will also help to further develop machine learning models
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working with NLP in general and LLMs in particular. They will also help to further develop machine learning models to predict clinical outcomes. Familiarity with current methods in this area is essential
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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About the Role A fantastic opportunity has arisen for a Senior Research Fellow to join the Power Electronics, Machines and Drives Research Institute (PEMC) at the University of Nottingham and become
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Vision conferences, CVPR, ICCV, ECCV and peer reviewed journals. Minimum Qualifications: PhD in Computer Science or a related field obtained within the last five years. Strong skills in machine learning
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area of expertise. You may be a great fit if: You are a passionate researcher with a PhD in Computer Science or a related field, experienced in machine learning for spatial data management, with a track
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-performance computing, machine learning models (eg. LLM), probabilistic models for data, novel techniques for making measurements, visualization tools, and community-oriented foundational software tools. Please
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research, oncology microbiomes, or environmental resistome surveillance. Familiarity with spatial metagenomics, single-cell microbiome analysis, or multi-omics data integration. Knowledge of machine learning