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spatiotemporal data, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference
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), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum message length (MML) (Wallace and Boulton, 1968; Wallace and Freeman, 1987; Wallace and Dowe, 1999a; Wallace
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), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum message length (MML) (Wallace and Boulton, 1968; Wallace and Freeman, 1987; Wallace and Dowe, 1999a; Wallace
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: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal prediction). O3: Joint OOD detection + AL: Combine
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: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal prediction). O3: Joint OOD detection + AL: Combine
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feeds M2 Internship – Perturbation-based Bayesian learning with resistive memories 107 / 799 vacancy Vacancy details Position description Category Micro and nano technologies Contract Internship Job
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based on these models. Performing parameter estimation and uncertainty quantification using Bayesian inference techniques based on experimental and/or synthetic data. Investigating efficient data
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Designing, building, and operating novel environmental sensors for basic research and science communication Building models and sensors for the estimation of ecosystem carbon exchange in urban and
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infrastructure behind our digital twins. You will: develop and benchmark methods for parameter estimation and uncertainty quantification, from classical optimisation and Bayesian inference to physics
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focused on estimation of parameters relevant to dynamics and control using innovative statistical methodologies. The advent of new sequencing technologies will revolutionise the way we study epidemiology