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Séverine Liegeois is currently doing a M2 internship on the visualization of modern and ancient DNA data through PCA or related techniques.
Jean Cury started a postdoc in January. He will work on the inference of demography and selection for bacterial populations using machine learning and deep learning techniques. This project is a collaboration with Philippe Glaser (Insitut Pasteur), Guillaume Achaz (MNHN) and Eduardo Rocha (Institut Pasteur) and is funded by DIM-1Health.
We are happy to announce that Théophile Sanchez obtained a bursary from ED STIC and is starting a PhD with us (Guilluame Charpiat, Marc Schoenauer and myself) to follow up his master internship Reconstructing the past: deep learning for population genetics!
The 2nd Junior Conference on Data Science and Engineering, Paris-Saclay will be hosted by the LAL on September 14th and 15th, 2017 (co-chairing with Sarah Cohen-Boulakia).
All the information on http://junior-data-science.org/
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Article sur la JDSE17
Guillaume Charpiat (TAU - INRIA/LRI) and I are pleased to welcome Théophile Sanchez, who is doing a master 2 internship thanks to a grant we obtained from the Center for Data Science.
Théophile is working on Reconstructing the past: deep learning for population genetics.

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You are a student interested in doing an internship about population genetics, statistics and machine learning? Feel free to contact me and tell me a bit about your experience and motivation!

Outdated: Together with Guillaume Charpiat (TAO - INRIA/LRI) we propose M2 internships on deep learning for population genetics. The proposals are focused either on more applied or more theoretical part of the work. Contact us to discuss what you think would suit you the best! We would be happy to have both biologists interested in stats/info/machine learning, or the other way around.
Fr: Reconstruire notre passé : apprentissage statistique (deep learning) pour la génétique des populations.
Fr: Architectures souples pour le deep learning avec application en génétique des populations.
En: Reconstructing the past: deep learning for population genetics.