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model and calibrate it using the newly acquired data. Lead the application of machine learning approaches to identify patterns in cassava yield from farm-level data, data captured from field sensors as
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to relevant research case studies within the AI4CI Research Hub. Developing data pipelines, machine learning models, and smart agents in simulated and real environments to develop and test hypotheses around the
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more than one year (or part-time equivalent) are NOT eligible to apply for an award. Recommended reading https://www.england.nhs.uk/2019/06/nhs-aims-to-be-a-world-leader-in-ai-and-machine-learning-within-5
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3 Jun 2024 Job Information Organisation/Company Université de Namur Department Physics department: Research unit for analysis by nuclear reactions (LARN) Research Field Computer science » Modelling
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pipelines, machine learning models, and smart agents in simulated and real environments to develop and test hypotheses around the application of AI in developing resilience to pandemics. Analysing historical
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collaborative, interdisciplinary setting. You must hold (or be about to receive) a PhD in Statistics, Data Science, Computer Science, or a related discipline. Substantial experience applying machine learning
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will initially be focused on developing and evaluating machine learning and statistical modelling tools to predict and classify disease trajectories using large scale health records databases to answer
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have a PhD in a relevant area as well an appropriate track-record of high quality research outputs in the field of Artificial Intelligence / Machine Learning, and clear and ambitious plans for future
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of experts in machine learning and colour vision, including Dr Alexandros Koliousis (NU London), Professor Rhea T. Eskew (NU Boston), and Professor Andrew Stockman (UCL), as well as with technical
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frameworks (MOF), a reticular class of materials for their use as excipients. The project also involves elements of machine learning models for screening appropriate excipients from FDA data base. Person