Theoretical and Observational Aspects of Early Universe Cosmology and Dark Matter

Research fellowship focusing on: 

The analysis and understanding of how dark matter, neutrinos and other particles/fields impact the evolution of the Universe via machine learning methods; improvement of the estimation of the dark matter mass profile of galaxies and clusters with machine learning methods; estimation of the mass-function of the dark matter subhalo population from strong lensing images using deep learning methods; optimization of Bayesian computation with machine learning forward models; development of Simulation-Based Inference methods for dark matter observations and experiments, particularly direct detection.