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Field
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to integrate heterogeneous molecular data, but are often less explicit about biological directionality and causal inference. This project instead builds on the structure of the central dogma, using genetic
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are essential, along with the ability to build and extend statistical pipelines. An interest in Bayesian inference applied to biology is also important. A background in computational proteomics or LC-MS/MS
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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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Computational Systems Biology group and has extensive expertise in Bayesian inference for biological systems. Project description Ordinary differential equation (ODE) models provide interpretable descriptions
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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inference problems with Bayesian statistics, powerful MCMC methods have been proposed, for example the MCMC differential evolution and the Riemann Manifold Langevin Monte Carlo methods. Because
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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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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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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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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