Dear (young) colleagues,
As every year, CNRS (and INRIA) is opening nation-wide positions in every field of science (for CNRS, INRIA focuses mostly on computer science and applied mathematics). For most people here, this would mean positions available in Philosophy, Economy, Mathematics and Computer Science, but maybe others. Those positions are permanent and full-time research positions, with a lot of flexibility in how you manage your time and activities.
CNRS candidates typically have to produce a summary of past research and a research project for the next 5-10 years, and give three teams or laboratory (these have to be joint research units, but there are such research units in many places in France).
For computer science, the official website is there: https://www.ins2i.cnrs.fr/en/cnrsinfo/researchers-competition-2025-list-posi...
And you can fetch some useful info there: https://mathurinm.github.io/cnrs_inria_advice/
In computer science, recruited candidates are typically young researchers, i.e., having less than 7-8 years of research experience since the beginning of the PhD thesis (but there can be exceptions). This may change if you want to apply, e.g., in economy or philosophy, as every field has its particularities.
If you wish to apply, you should try to identify and contact ASAP a laboratory in France where you would like to work (e.g., by just looking up in which laboratory is your favourite French researcher working), and contact them. Laboratories that have people dealing with imprecision in uncertainty or that would be interested in candidates working in these fields include mine, but also the CRIL (Lens), IRIT (Toulouse), GREGHEC and LAMSADE (Paris), and a couple others! Deadline for applications are beginning of January, but putting up a good application file takes quite some time and efforts.
I can try to reply to your possible questions if you have some. However, I will mostly have information about the committees and processes concerned with computer science, automatic, robotic, signal treatment, machine learning and uncertainty propagation.
Best
Sébastien