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We are looking for a Research Fellow to improve the NEEDLE machine learning code for classifying astrophysical transients and manage spectroscopic follow up. The successful applicant will work with
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cardiovascular care using advanced machine learning techniques, including deep learning. Informal enquiries may be directed to Dr. Dimitrios Doudesis, Principal Investigator (Dimitrios.Doudesis@ed.ac.uk
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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, alongside a collaborative approach. Prior experience in computational electromagnetics modelling, the application of machine learning algorithms, and development of precise optical experiments including
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imaging, including medical imaging and digital pathology, data using cutting-edge AI and machine learning approaches. The ideal candidate will play a critical role in integrating diverse data sources
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and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially explicit
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Language Processing (NLP) with a focus on large language models, deep learning, and multi-modal machine learning. The researcher will work on the project KAMAL Health: Knowledge-Augmented Multi-Modal Arabic LLMs
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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(particularly under extreme conditions), and/or the use of machine learning for solid mechanics/stress analysis problems are encouraged to apply. The job description presented here is deliberately broad due