Connectionists: PhD/postdoc position (fully funded) in resource-efficient probabilistic inference

[Luigi Acerbi]

Dear all,

I am looking for a postdoc or PhD student in *Resource-Efficient
Probabilistic Machine Learning*, to join our group
<https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence> at
the Department of Computer Science, University of Helsinki, with
applications in model fitting of complex computational models in cognitive
science and AI.

The position is full-time, funded for 4 years (PhD) or 2+2 years (postdoc)
and will be filled as soon as possible, with a starting date in autumn
2023. The starting salary will depend on the position (PhD or postdoc) and
previous qualifications and experience. Applications will be considered
until the position is filled.

*For the full ad:*
https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence/phdpostdoc-position

*Brief project description:*

The main goal of the project is to extend and improve on our VBMC framework
for efficient probabilistic inference with moderately-to-very expensive
models, published in multiple papers (e.g., Acerbi, NeurIPS 2018
<https://arxiv.org/abs/1810.05558>; Acerbi, NeurIPS 2020
<https://arxiv.org/abs/2006.08655>), available in MATLAB
<https://github.com/acerbilab/vbmc> and recently released for Python
<https://github.com/acerbilab/pyvbmc>.

We aim to perform Bayesian inference for parameters of complex, expensive
state-of-the-art models in fields such as cognitive science and AI. An
example is the AI-inspired model of human gameplay from Wei Ji Ma's group (van
Opheusden et al., Nature 2023
<https://nature.com/articles/s41586-023-06124-2>).

The project includes funding for research visits to international
collaborators such as Wei Ji Ma <https://www.cns.nyu.edu/malab/> at New
York University and Michael Osborne <https://www.robots.ox.ac.uk/~mosb/> at
the University of Oxford. We also have many local collaborators, such as Antti
Honkela <https://www.cs.helsinki.fi/u/ahonkela/> for applications of
sample-efficient inference to privacy, and our team is highly involved in
the thriving & highly collaborative community of probabilistic ML/AI
researchers — PhDs, postdocs, PIs — in the Finnish Center for Artificial
Intelligence FCAI <https://fcai.fi/>, on top of many ongoing national and
international collaborations in cognitive science and computational
neuroscience.

Best wishes,
Luigi

--
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
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