Connectionists: 5 postdoc positions in Deep Learning and Riemannian Stochastic Optimization at RIST

[Luigi Malagò]

[Our apologies for cross postings]

The Romanian Institute of Science and Technology (RIST) has an opening for
5 postdoc positions, in the context of the DeepRiemann project “Riemannian
Optimization Methods for Deep Learning”, funded by European structural
funds through the Competitiveness Operational Program (POC 2014-2020). The
appointments will be for 1 year, with possible extensions up to 3.5 years.

The DeepRiemann project aims at the design and analysis of novel training
algorithms for Neural Networks in Deep Learning, by applying notions of
Riemannian optimization and differential geometry. The task of the training
a Neural Network is studied by employing tools from Optimization over
Manifolds and Information Geometry, by casting the learning process to an
optimization problem defined over a statistical manifold, i.e., a set of
probability distributions. The project is highly interdisciplinary, with
competences spanning from Machine Learning to Optimization, Deep Learning,
Statistics, and Differential Geometry. The objectives of the project are
multiple and include both theoretical and applied research, together with
industrial activities oriented to transfer knowledge, from the institute to
a startup or spin-off of the research group.

The positions will be part of the new Machine Learning and Optimization
group www.luigimalago.it/group.html, which will be performing research at
the intersection of Machine Learning, Stochastic Optimization, Deep Learning,
and Optimization over Manifolds, from the unifying perspective of
Information Geometry. The group is one of two newly-formed groups in
Machine Learning at RIST, where about 20 new postdoctoral research
associates and research software developers will be hired in the next year.

The open positions will focus on different and overlapping aspects of the
project:

1) Optimization Algorithms over Statistical Manifolds with Applications to
Deep Learning
2) Theory of Neural Networks
3) Information Geometry of dually-flat Hessian Manifolds
4) Training of Neural Networks using Riemannian geometries.
5) Information Geometry of Deep Generative Models
6) Natural Policy Learning for Deep Reinforcement Learning.

The positions are to start as early as May 2017 or at any agreed later
date. Applications will be reviewed as they are received.

More information can be found at the following links:
a)
http://rist.ro/en/details/news/postdoc-positions-in-machine-learning-optimization-deep-learning-and-information-geometry.html
b)
http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machine-learning.html

best regards,
Luigi Malagò

-- 
Luigi Malagò
Principal Investigator
Romanian Institute of Science and Technology - RIST
Machine Learning and Optimization Group
Address: Str. Virgil Fulicea nr. 17, 400022 Cluj-Napoca, Romania
Homepage:  http://www.luigimalago.it
Office: +40 364 408794 <+40%20364%20408%20794>
Mobile RO: +40 758 244646 <+40%20758%20244%20646>
Mobile IT: +39 340 6249299 <340%20624%209299>
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