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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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, cluster randomised controlled trials implementation science, data linkage, data science, machine learning and artificial intelligence. In this role, you will have the opportunity to engage in a series of
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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strong analytical skills and desirably some computer modelling experience, and an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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the very people who bring those lessons to our students. Every bit of knowledge, and every advancement, provides our students with an unprecedented, collaborative learning environment, and helps improve
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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. This group uses state-of-the-art Earth observation data and advanced computer techniques to study the Polar regions. We specialise in using Synthetic Aperture Radar (SAR) and altimetry satellite data