DATAHYKING unites researchers from 7 research institutes (KU Leuven, Università degli Studi di Ferrara, Università di Roma Sapienza, RWTH Aachen, Technische Universität Kaiserslautern, Université de Lille and INRIAi). This consortium will create a data-driven simulation framework for kinetic and hyperbolic models of interacting particle systems and define a common methodology for these future modeling and simulation experts.

The 13 PHD positions:

1. Numerical methods for merging scales in hyperbolic problems
2. Analysis and numerical methods of consensus-based optimization techniques
3. Robust parameter estimation and control of novel models for financial markets
4. Forward uncertainty quantification for hyperbolic and kinetic equations
5. Robust consensus-based optimization for machine learning
6. Microscopic and macroscopic models for multilane and multispecies traffic flow
7. Interactions between microscopic and macroscopic models for autonomous vehicles in human-driven environments
8. Bayesian model calibration with uncertainty for traffic flow models
9. Advanced discretization methods for hyperbolic and kinetic equations with moving boundaries
10. High-order and hybrid domain decomposition methods for hyperbolic and Boltzmann equations
11. Accelerated training methods for deep learning based on mean-field limits of neural ODEs
12. Particle methods for granular flow: numerical methods and adjoint based optimization for parameter identification
13. Modeling and optimization of multi-modal transportation networks based on kinetic and hyperbolic equations

For more information and how to apply, please visit the Datahyking website: https://datahyking.eu/?page_id=259