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analysis and a basic knowledge of causal inference would be an advantage. Training and support in new areas will be provided. The appointed individual will be based at the Leicester Diabetes Centre
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employing Regression Discontinuity Designs (RDD) and related causal inference models, with a strong emphasis on advancing statistical methodology. The project presents significant methodological challenges
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integrating large complex datasets is likewise essential as is training in Bayesian sample assessment methods. The post is based in UCC and requires the successful candidate to work closely and in person with
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metabolism in the human fungal pathogen Cryptococcus neoformans Jonathan Doucette Doctor of Philosophy in Physics (PhD) Probing Brain Tissue Microstructure with Magnetic Resonance Imaging through Bayesian
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Differentiation Nikola Surjanovic Doctor of Philosophy in Statistics (PhD) Scalable Bayesian Methods for Sampling From Complex Probability Distributions Katrina Besler Doctor of Medicine and Doctor of Philosophy
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and implementing appropriate empirical strategies for causal inference, particularly in econometric analyses. Assist in designing new econometric models or adapting existing ones to address evolving
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, statistical forecasting and inference, and detection/classification. For cyber-focused positions within the Intelligent Systems Division, individuals with experience in cybersecurity, hardware/software reverse