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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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of the following topics will be appreciated, but mostly we look for smart people who enjoy learning new things: Approximate Bayesian inference Differential geometry Numerical computations (ideally with experience in
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models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection
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quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with computer vision tools (e.g., MediaPipe
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of models of co-infection (including parameter estimation, model calibration, validation, etc.) and close collaboration with researchers, clinicians, and public health partners. Professor Hollingsworth’s
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
and data-driven procedures for pointwise and/or credibility interval estimation of epidemiological indicators, e.g., for the reproduction number R(t) of Covid19. Elaborating on a recent epidemiological
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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estimation of adverse Zika outcomes while addressing measurement biases. We aim at generating evidence to inform public policy, healthcare providers and pregnant individuals, improving prevention during
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to the development of Bayesian inference frameworks that use GATES. The postholder will develop machine learning models of atmospheric transport and use them in Bayesian inverse modelling frameworks to estimate
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Description Distribution estimation algorithms for abductive inference (total or partial) in dynamic domains. Structural learning of dynamic Bayesian networks with discrete and continuous variables (parametric