Postdoctoral Researcher (M/F)

Descrizione

Postdoctoral Researcher (M/F) Sintesi in italiano (traduzione automatica): L'organizzazione cerca un Ricercatore post-dottorato per il team di astrofisica del Laboratoire de Physique de l'École normale supérieure (LPENS) a Parigi, nell'ambito del progetto ANR ForeSight. Il candidato selezionato si occuperà di ricerca post-dottorale, sviluppando approcci innovativi per separare i B-modes primordiali della radiazione cosmica di fondo dalle interferenze galattiche. Le mansioni principali includono la progettazione e implementazione di nuovi algoritmi, la validazione di metodi esistenti e l'applicazione di queste tecniche a dati reali. È richiesta una laurea in fisica o un campo correlato, con competenze in astrofisica, matematica applicata e scienza dei dati. Il candidato parteciperà a riunioni scientifiche e presenterà i risultati in conferenze e pubblicazioni. The successful candidate will carry out postdoctoral research within the LPENS astrophysics team, as part of the ANR ForeSight project, which develops innovative approaches to separating the primordial B-modes of the cosmic microwave background from polarized Galactic microwave foregrounds. The precise research topic will be defined jointly with the team at the start of the contract, depending on the candidate's skills and interests and the progress of the project.Two main axes structure the project and will serve as a starting point for defining the topic: (i) extending ST-based component separation to multi-frequency, multi-component configurations, in connection with the application to detecting primordial CMB B-modes; (ii) positioning these methods relative to classical component-separation approaches, both through validation and hybridization with these approaches, and through the development of new ST algorithms built from scratch where existing methods fall short. Contribute to the development of non-Gaussian component-separation algorithms based on Scattering Transforms, in particular in multi-frequency configurations (extending the framework developed in Tsouros et al. 2026). Study how ST methods relate to classical component-separation approaches (GNILC, SMICA, NILC, Commander, parametric approaches): comparison, cross-validation, and hybridization where relevant. Design and implement new ST algorithms not covered by existing approaches, as needed by the project. Contribute to applying these methods to real data (Planck, and eventually Simons Observatory / LiteBIRD) on polarized Galactic foregrounds (dust, potentially synchrotron). Use and contribute to the STL Python library, the team's shared software foundation for Scattering Transform computations. Take part in the team's scientific meetings (weekly meetings, Scattering Club seminar) and in exchanges with project collaborators. Work closely with other team members on related topics (component separation, Bayesian inference in ST space, software development). Regularly present progress within the group and at external conferences or seminars, and disseminate results through publications in peer-reviewed journals. The position is based at the Laboratoire de Physique de l'École normale supérieure (LPENS), within the astrophysics team, with a possible affiliation to the ENS-PSL Center for Data Science. The working environment is interdisciplinary, at the interface between astrophysics, applied mathematics, and data science. The ANR ForeSight project (2025–2029) aims to build generative models of polarized Galactic foregrounds directly from observational data, building on Scattering Transforms, with a view to their application to detecting primordial CMB B-modes. The successful candidate will join an already established team (several PhD students and postdocs, with recent work on polarization component separation and on Bayesian inference in ST statistics space), and will benefit from the existing software infrastructure (STL library) and computing resources (the team's GPU cluster, access to national facilities such as IDRIS) already in place. Annuncio in inglese. Fonte: Euraxess (Commissione europea).