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Field
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stochastic processes, Markov models, dynamical systems, quantum walks, or related mathematical approaches. Experience with computational model fitting, Bayesian inference, simulation, or formal model
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in mathematical modelling and Bayesian inference while learning from three collaborating chief investigators. You will also build your publication record and professional networks through seminars
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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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cancer cohorts. • Proven ability to develop novel computational and AI-driven methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data
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—or a strong affinity with—Bayesian Statistics, who is eager to contribute to our overarching research programme in high-dimensional statistics. As Associate Professor, you will play a leading role in
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, including reinforcement learning, hierarchical models, Bayesian inference etc. More details of key responsibilities of this role, in addition to the essential and desirable job criteria, are available in
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, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference, ● methods
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Moss to: Develop machine learning emulators for the WAVI ice-sheet model to serve as efficient surrogates for large-scale Bayesian inference. Develop utility-function-based experimental design methods
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statistical approaches that improve inference across space, time, and taxa. The selected postdoctoral associate will be an integral part of advancing the integration of acoustic and eBird datastreams, improving
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be based at Lancaster and work with Dr Henry Moss to: Develop machine learning emulators for the WAVI ice-sheet model to serve as efficient surrogates for large-scale Bayesian inference. Develop