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Postdoc position in Geometric Methods for Deep and Reinforcement Learning at RIST

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 http://luigimalago.it/group.html, which performs 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 by 2018. 


The official job announcement can be seen here:

http://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machine-learning.html


Informal inquiries can be sent to Dr. Luigi Malagò <malago@rist.ro>, principal investigator of the DeepRiemann project. 


Application deadline: 06 May 2018 (applicants are encouraged to apply earlier)


CountryRomania
LabCluj-Napoca
Duration/Period1 year, possible extention to 2.5 years
Deadline06/05/2018 0:00
Linkhttp://rist.ro/en/details/news/postdoc-positions-in-deep-learning-and-machine-learning.html
KeywordsDeep Learning; Reinforcement Learning; Stochastic Optimization; Optimization over Manifolds; Information Geometry; Riemannian Geometry;

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