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Assistant Professor positions beginning August 16, 2027. We seek outstanding candidates with research interests in causal inference, Bayesian computation, statistics, machine learning, or related areas
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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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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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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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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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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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, Bayesian analysis, modern causal inference, biomarkers analyses, statistical genetics and genomics, computational statistics, systematic review methodology, structural equations modeling, and muti-omics
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statistics, or decision science, who specializes in one of the following areas: statistical learning theory, causal inference, Bayesian statistical modeling, or AI and data management. [Desirable