*Doctoral position in Sample-Efficient Probabilistic Machine Learning (4 years, fully funded)* The Machine and Human Intelligence research group <https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence> led by Assistant Professor Luigi Acerbi is looking for a PhD candidate eager to work on new machine learning methods for smart, robust, sample-efficient probabilistic inference, with applications to computational and cognitive neuroscience. The candidate will join a newly established research group at the Department of Computer Science of the University of Helsinki (Finland) with strong links to the Finnish Center for Artificial Intelligence (FCAI) <http://fcai.fi/>. Our group is developing novel machine learning approaches for approximate Bayesian inference that use only a small number of likelihood evaluations, which can be a game-changer for complex computational models or when resources are limited. A state-of-the-art framework being developed in our group is Variational Bayesian Monte Carlo (VBMC) <https://github.com/lacerbi/vbmc>, which combines Gaussian process surrogates, active learning, variational inference and Bayesian quadrature (Acerbi, *NeurIPS*; 2018, 2020). Promising thesis projects include extending the representational power of VBMC (e.g., discrete variables, more complex posteriors, higher dimension); exploiting recent advances in Gaussian process inference for superior scalability; combining VBMC with Bayesian deep learning; strengthening the connections with simulator-based inference; and exploring the theoretical properties of the framework. The position is full-time, funded for four years and will be filled as soon as possible, with a negotiable starting date in early 2021. The starting salary is 2350-2700 euros/month, depending on previous qualifications and experience. Application deadline: *January 10, 2021*. For more details and how to apply, see: https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence/phd... -- Luigi Acerbi, Ph.D. Assistant Professor of Machine and Human Intelligence Department of Computer Science, University of Helsinki Lab: http://www.helsinki.fi/machine-and-human-intelligence