Past conferences, workshops and schools

2024


Workshop on Numerical analysis, control, and optimization for physics
  • Title: Numerical analysis, control and optimization for physics
  • Date: September 18th – September 19th, 2024
  • Location: Inria, Sophia Antipolis, France
  • Organizers: Laetitia Giraldi et Laurent Monasse
  • More information on the workshop website

Mascot-Num

2023


Lectures on Burgers turbulence
  • Title: Burgers turbulence
  • Date: March 6th, March 11th & March 18th 2023
  • Location: Coriolis room (Galois building, Inria)
  • Lecturer: Jeremie Bec

2022


EMRSIM Conference
  • Title: Simulation and Optimization for Marine Renewable Energy
  • Date: May 30th – June 2nd 2022
  • Location: Roscoff, France
  • Organizers: Mireille Bossy, Martin Parisot, Antoine Rousseau, Julien Salomon
  • Website: emrsim2022.sciencesconf.org
  • Important deadlines:
    • Abstract submission: April 15th, 2022
    • Registration: May 30th, 2022
  • Announcement: The aim of EMRSim is to promote exchanges between researchers in scientific computing and mechanics and industrialists specializing in marine renewable energy.  The covered topics will be related to the simulation and numerical optimization of marine energy extraction devices such as tidal turbines, offshore wind turbines or wave-engine systems.

2021


Workshop on microplastics

UCA Fall program on Complex Systems 2021
  • Title: Mobility, self-organization and swimming strategies
  • Date: October 18th – October 29th 2021
  • Location: Nice and Fréjus, France
  • Organizers: Jérémie Bec, Laetitia Giraldi, Fernando Peruani
  • Website: https://fox2021-uca.sciencesconf.org/
  • Important deadlines:
    • Application: September 15th, 2021
  • Announcement: The Fall School is open to all PhD students, post-doctoral and permanent researchers interested in mobile active matter and wishing to get involved in a collaborative work in one of the following topics:
    • Swimming into complex environment – micro-swimming
    • Collective motion
    • Machine learning applied to active particles