À: « info statphys » <firstname.lastname@example.org>
Envoyé: Mardi 20 Novembre 2018 13:04:08
Objet: [info.statphys] Postdoc in data-driven modeling: theory and applications
We are looking for a post-doc candidate at the crossroad of statistical
physics, machine learning, and computational biology, interested in the
theoretical and applied aspect of data-driven modeling.
Thanks to recent progress in machine learning, machine learning can be
used to establish models of complex systems, which remain out of reach
with standard first-principle methods. The goal of the post doctoral
project will be two-fold:
(1) develop unsupervised machine learning tools and apply statistical
physics methods and concepts to better understand how these methods
operate and learn from data. Different unsupervised architectures will be
studied and compared, including Boltzmann Machines, Restricted Boltzmann
Machines, and (Variational) Autoencoders.
(2) apply these methods to model proteins from sequence data, with special
emphasis on the prediction of mutational effects and mutational paths in
the trypsin enzyme, in connection with high-throughput experiments by C.
Nizak and O. Rivoire at College de France.
The post-doc will be located in the Department of Physics at the Ecole
Normale Superieure in Paris, under the supervision of S. Cocco and R.
Monasson. The duration of the position is of two years. Post-doc
candidates are expected to have solid knowledge in statistical physics,
inference methods and data analysis, and both analytical and computer
programming skills. Moreover he/she should have a deep interest and
possibly a previous experience in computational biology and/or
Applications should be sent by email to email@example.com or
firstname.lastname@example.org by January 15, 2018.
Please help us distribute this announcement.
Simona Cocco and Rémi Monasson
To submit a message, please send it directly to email@example.com
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