A postdoctoral position in Statistical Mechanics/Machine Learning/RNAesign and evolution is available at the Physics Departement of the Ecole Normale Superieure in the team of S. Cocco and R.Monasson
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 , 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:
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 Autoencoders. A special attention will be devoted to built interpretable and easily samplable models, being able to disentangle representations using partially annotated data.
Apply these methods to model the evolution and switches of ligand-specificities in RNA riboswitches, in connection with experiments by B. Sargeuil (Paris Descartes) and bioinformatics development by the team of Y. Ponty (Ecole Polythecnique) thanks to a joint ANR project.
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 one year, extensible to 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 bioinformatics.
References to published papers.