PhD or PostDoc position on simulation-based inference with Paul “brms” Bürkner

Hi all, this is Paul. Andrew was so kind to allow me to post a job ad here on his blog.

At the Technical University of Dortmund, Germany, I am currently looking for a PhD Student or PostDoc to work with me on simulation-based Bayesian inference research in the context of our BayesFlow framework.

BayesFlow is a Python library for efficient simulation-based Bayesian Inference. It enables users to create specialized neural networks for amortized Bayesian inference, which repays users with rapid statistical inference after a potentially longer simulation-based training phase. A cornerstone idea of amortized Bayesian inference is to employ generative neural networks for parameter estimation, model comparison, and model validation when working with intractable simulators whose behavior as a whole is too complex to be described analytically.

Both the BayesFlow library itself and its community are quickly growing. Our goal is to make it the gold-standard simulation-based inference library within the next couple of years.

For more details about the position, please see Paul Bürkner – Open Positions

I am looking forward to your applications!


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