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requires a high level of judgment, scientific initiative, and technical skill in geospatial data processing, as well as the ability to manage multiple deadlines and prioritize tasks. The successful candidate
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University's Department of Mechanical Engineering
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Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above. We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is...
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS
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Job description: We are currently advertising for a Research Associate/Research Fellow in Causal Inference & Health Technology Assessment (HTA) to join the Health Economic and Decision Science (HEDS
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Post Summary The Characterisation & Processing of Advanced Materials Research Group, led by Professor Valeria Nicolosi, is currently seeking a Research Fellow with significant practical experience
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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Universities; and #8 for Data Science and AI by QS World University Ranking. A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems