Sort by
Refine Your Search
-
simulations (integrated modelling) can be used to better understand the dependencies obtained in scaling laws and better constrain them beyond the existing experimental space. This is the goal of the present
-
on the propagation of social movements and other diffusion phenomena (epidemics, dissemination of opinions or false information). Develop modeling that may use epidemic propagation or reaction-diffusion models, or any
-
trace them back to incident particle fluxes using GEANT-4 simulations of the instrument. These analyses will improve the calibration of the instrument and better constrain the presence of particles
-
of quantum gases; Anderson problem - Numerical simulations of disordered and chaotic quantum dynamics This postdoctoral project focuses on studying a newly observed subdiffusive transport mechanism in
-
an approach based on 3D imaging and numerical simulation. The objective is to support the development of the most "infiltrable" ex-PIP matrices by characterizing the spatial organization of these porous media
-
the framework of the ANR EmergeNS whose aim is to understand, through mathematical and computer models, the role that autocatalysis, multistability and spatial heterogeneity may have played in the emergence
-
to improve the energy resolution of jets at FCCee. This task will require again GEANT4 simulations of the detector, including the optical model but necessitates as well to educate the simulation with
-
self-supervised learning model, -Evaluating model performance using both simulations and experimental data, -Transitioning from a task-specific model to a foundation model, -Benchmarking results against
-
of balancing selection is. In this context, we have launched the BalanSe ANR to better understand the importance of balancing selection in non-model organisms. This project will rely on a combination of
-
Team : We seek a researcher with demonstrated experience in perturbative modeling of LSS (in particular biased tracers of dark matter), analysis of simulated datasets, and strong programming in Python