Sort by
Refine Your Search
-
defined based on your profile and skills, ensuring a strong fit with the project's needs and your own expertise. As a key collaborator, you will work alongside me and two PhD students, taking the lead on
-
” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
-
be supervised by Johan Decelle. There will be numerous interactions and synergies with national and international partners. To learn more about the project, several publications are available
-
, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
-
of the Earth and the near Universe. It comprises about 170 people (Researchers, teacher-researchers, engineers, PhD students...). It is spread over 2 sites: the CNRS Azur campus in Valbonne Sophia Antipolis and
-
physics groups, as well as the DELPH (Detection and Lasers for Physics) and GTA (Acquisition Techniques Group) groups. The Physics Division is composed of 25 permanent physicists, around 20 PhD students and
-
in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in
-
plus - Good writing skills and oral expression in english (at least B2) - Scientific rigor and curiosity Technical skills : - Required: PhD or engineering degree in deep learning - Required: good
-
copolymers, which will then be evaluated for their degradability and mechanical properties. Using active learning, a branch of AI, the research will be guided through the large parameter space of copolymers
-
experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP