Laurent Jacques

This four-year fully funded PhD position at Université catholique de Louvain (UCLouvain), Belgium, is focused on gravitational-wave (GW) data analysis using machine learning (ML) techniques. Hosted at the Centre for Cosmology, Particle Physics and Phenomenology (CP3) within the Research Institute in Mathematics and Physics (IRMP), and in collaboration with the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), the project centers on applying neural posterior estimation to analyze compact binary coalescence (CBC) signals in current and future GW detectors. The UCLouvain GW group is an active member of the Virgo Collaboration at the European Gravitational Observatory and participates in a range of research areas, including ultra-light dark matter searches, anisotropic stochastic GW background, gravitational wave lensing, and multi-messenger astronomy. Data analysis is performed in close collaboration with theoretical physicists and leverages the IRMP computing center, which also supports major international experiments such as CERN's CMS and NA62, IceCube, and KM3NeT. The group is also involved in instrumentation projects for both Virgo and the upcoming Einstein Telescope, with research and development at E-TEST and ETPF facilities. The selected PhD candidate will join the GRAVIT-AI consortium, working under the joint supervision of experts in GW physics and ML: Giacomo Bruno, Laurent Jacques, Justin Janquart, and Estelle Massart. The environment is highly international and collaborative, with strong support from postdoctoral researchers and excellent computing and travel resources. The UCLouvain campus is located near Brussels, offering convenient access to both city and countryside living. Personnel benefits include public transport reimbursement and free language courses. Applicants should have a Master's degree in physics or equivalent, a strong academic record, and a keen interest in fundamental physics research. Good knowledge of GW physics and coding skills are advantageous. Applications require a CV, motivation letter (with topic preference if any), academic transcript, and at least two recommendation letters. The application deadline is 30 November 2025, but the position will remain open until filled. For more information, contact Justin Janquart ([email protected]).