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learning models such as Bayesian optimization, neural networks, random forests. A high proficiency in spoken and written English. Excellent communication and interpersonal skills. You are expected to learn
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; a global network of campuses and partners for students and faculty to leverage for learning and research; a deep investment in lifelong and experiential learning; a premium placed on pedagogical
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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; a global network of campuses and partners for students and faculty to leverage for learning and research; a deep investment in lifelong and experiential learning; a premium placed on pedagogical
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Job Purpose To make a leading contribution to the ECOCHANGE (Ecosystem Consequences of Changes to Habitats and the implications for a Net Gain Energy approach) project working with Dr Kieran Tierney
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such as imaging, spatial, network, and genomics data. The appointment is expected to begin on August 15, 2026. Review of applicants will begin on November 17, 2025, but the position will remain open until
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
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randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
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; maintain version control and prepares data for submission to public repositories and collaborative networks. Conduct statistical and spatial analyses of ecological and climate datasets. o Implement
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces