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field. Preference will be given to candidates with: Previous experience in machine learning-related aspects of computational neuroscience, specifically with approximate Bayesian inference, and function
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exploration strategies that go beyond traditional techniques such as linear programming or deterministic solvers. You will work on cutting-edge methods including: Bayesian optimization Surrogate modeling
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include Bayesian data analysis, nonparametric statistics, functional data analysis, spatio-temporal statistics, and machine learning/artificial intelligence. Many of our projects involve dynamic processes
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analysis Developing methods to improve the accuracy and robustness of parameter estimation and uncertainty quantification using Bayesian techniques Applying the developed methods to calibrate and validate
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and carbon cycle model-data integration using the CARDAMOM Carbon-Water Bayesian model-data integration framework. The candidate will help advance global land biosphere estimates of biomass, water
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, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging
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When a large number of factors are considered in an experiment, the identification of active factors that may have a substantial impact on the outcome is needed for screening purposes. Computer
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. To combine the data and models, and estimate uncertainties, they will develop and use Bayesian “inverse modelling” techniques. You will work closely with a team of around 10 researchers in the ACRG studying
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simulations and data. To combine the data and models, and estimate uncertainties, they will develop and use Bayesian “inverse modelling” techniques. You will work closely with a team of around 10 researchers in
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. The AMR sub-team estimates the global burden of drug-resistant and susceptible infections, including their geographic distribution and clinical impact. Both sub-teams rely on diverse data sources