Connectionists: PhD/Postdoc positions in probabilistic machine learning (University of Helsinki)

[Luigi Acerbi]

Dear colleagues,

Please find below several positions available in our group.


*1) 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 scientific modeling (e.g.,
computational neuroscience) and artificial intelligence. 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/>.

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-student-position

*2) Postdoctoral positions in Probabilistic Machine Learning*

The Machine and Human Intelligence research group
<https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence> led
by Assistant Professor Luigi Acerbi has several openings for co-supervised
postdoc positions as part of a Finnish Center for Artificial Intelligence
(FCAI) - ICT Helsinki joint postdoc call. Projects in our group relate
to sample-efficient and active-sampling approaches in probabilistic machine
learning. The candidates will join a research team of other FCAI postdocs
and professors based at the University of Helsinki and Aalto University
(Finland).

Research topics in our group include: (#23) "Data augmentation, noise and
active learning - A Bayesian brain approach", co-supervised with Aapo
Hyvärinen; (#24) "Bayesian deep active learning for amortized inference of
simulator models", co-supervised with Samuel Kaski & Jukka Corander; (#25)
"Fast active-sampling approximate Bayesian inference for everyone",
co-supervised with Aki Vehtari, Samuel Kaski & Arto Klami.
Application deadline: *January 25, 2021*.

For the full list of 40+ topics and how to apply, see:
http://www.hiit.fi/postdocs


-- 
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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20201219/f97eea8f/attachment.html>