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Haslinger and Professor Jason Ralph. The team brings together Liverpool’s strengths in wave propagation and scattering in complex media, mathematical modelling, Bayesian inference and information fusion
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. The team brings together Liverpool’s strengths in wave propagation and scattering in complex media, mathematical modelling, Bayesian inference and information fusion, signal processing, tracking and the
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference
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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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, ● 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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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and