Contact / Available positions

firstname.name (at) cnrs (dot) fr

firstname.name (at) lri (dot) fr (this email is temporarily down, if you sent an email in August, I most likely never received it, please send it again to my cnrs address)
Laboratoire de Recherche en Informatique

Phone:
+33(0)1 69 15 42 18

Mailing address:
Flora Jay
LISN, CNRS UMR9015 
Université Paris Saclay
Digiteo Moulon Bâtiment 660
Avenue des Sciences
91190 GIF SUR YVETTE


Affiliation:
Université Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400, Orsay, France.

 
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Available positions

Contact me if you are interested in our lab's research in general, and in particular in

  • 1 postdoctoral position on ML for microbial genetics, prediction of antibiotic resistance from genetic sequences. Collaboration with Bogdan Iorga (ICSN-CNRS) and Philippe Glaser (Pasteur Institute). Start in 2024/2025.

  •  Population genetics/modeling/method development
    HFSP project - together with  Emilia Huerta-Sanchez lab (Brown University) and Maria Avila Arcos (UNAM, Mexico): "Evolutionary changes in human hosts and their pathogens during first contact in the New World".
    1 postdocotal position filled by Léo Planche
     


     
  • 1 PhD student for 2022 in deep learning for population genetics. filled by Antoine Szatkownik. Funded by ANR.
  • Internship position for 2020 (3-6 months) filled by Mathieu Michel. Testing deep learning methods for selection inference - comparison to state-of-the-art statistical approaches - application to pathogen genomics - DL architecture development (depending on your background). This project will be co-supervised by Jean Cury
  • 2 postdoctoral positions filled by Federica Pierini and Jean Cury (machine learning/population genomics/mathematical modeling/...) in my lab for our HFSP project (E Huerta-Sanchez U Brown, US & M Avila Arcos,UNAM, Mexico).
  • PhD funded by CNRS  filled by Jérémy Guez. "TransIA: Machine Learning based Inference of Cultural Transmission of Reproductive Success using Genomic Data"
    Details and application

 

Otherwise, you might be interested in the research of some previous/current collaborators:





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