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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven epidemic forecasting, transmission modelling in the ASEAN region
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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with researchers and practitioners through the aiD network and related national initiatives. Contribute to the scientific development and collaborative activities of the Data Science research group
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vehicles and achieving net-zero targets, but they degrade over time, reducing performance and safety. A key issue is gas generation, which causes internal pressure build-up and can lead to cell failure, but
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in complex biological networks e.g. food webs, forming the foundation of natural ecosystems. Yet, we lack the tools to predict how these networks change in time and space. This is especially critical
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible