> Markdown version of [/jobs/ext/2030649-job-offer](https://www.wearedevelopers.com/jobs/ext/2030649-job-offer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Job offer - **Company:** CNRS - **Location:** Nantes, France - **Contract:** Temporary contract - **Skills:** Machine Learning, Data Processing - **Published:** August 12, 2026 - **Apply:** https://emploi.cnrs.fr/Offres/Doctorant/UMR6457-SOPDEP-072/Default.aspx ## About the Role The candidate must hold a Master's degree in physics. They should: * Demonstrate strong motivation, curiosity, and independence. * Be capable of writing scientific reports and presenting results orally. * Be able to work both independently and collaboratively in an international team environment. Prior experience in particle or astroparticle physics, strong computational skills, and previous experience in simulations, data processing, and analysis will be considered assets. Proficiency in English is required. ## Description The PhD student will be based at SUBATECH (Nantes), a leading laboratory in particle and astroparticle physics, and will be enrolled in the PhD program of IMT Atlantique Nantes. The Xenon team at SUBATECH is heavily involved in dark matter and rare event research within the international XENON Collaboration, which operates the XENONnT experiment at the Laboratori Nazionali del Gran Sasso (LNGS), Italy. In this context, the selected candidate will be a member of the XENON Collaboration and will closely collaborate with the team at LPNHE (Paris), a partner in the ANR-funded LowDM project. In particular, the PhD student will: * Participate in detector operations, calibrations, and data-taking shifts at XENONnT. * Attend weekly analysis meetings with the XENON Collaboration's thematic groups. * Benefit from mentorship by experts in the collaboration. * Have the opportunity to present results at international conferences. Light Dark Matter Search with Data from the XENONnT Experiment The existence of dark matter (DM) is one of the deepest mysteries in modern physics. While Weakly Interacting Massive Particles (WIMPs) have long been the leading candidates, the absence of confirmed signals has shifted research toward low-mass candidates (sub-GeV), detectable via the Migdal effect. This effect, recently observed for the first time in neutron-nucleus collisions and published in Nature in 2026, allows nuclear recoils to produce detectable ionization signals, even below traditional energy thresholds. The XENONnT experiment, a dual-phase liquid xenon (LXe) time projection chamber (TPC) with a 5.9-tonne active target, is a cutting-edge instrument for exploring low-mass dark matter candidates. Recent upgrades, including the replacement of electrodes, have enabled improved signal collection and a significant reduction in background noise, thereby enhancing the experiment's sensitivity. The LowDM project, funded by the ANR (French National Research Agency), aims to leverage XENONnT's enhanced capabilities to probe the 0.03-3 GeV/c² mass range with unprecedented sensitivity. This thesis thus offers an exceptional opportunity to contribute to a major advance in astroparticle physics, with the potential to discover dark matter or, alternatively, to set the most stringent exclusion limits to date for low-mass candidates, thereby pushing the boundaries of our understanding of the Universe. The main objectives are: * Develop direct searches for light dark matter via the Migdal effect using new data from the XENONnT experiment. * Optimize signal reconstruction for ultra-low-energy events (a few electrons) and suppress instrumental background noise (e.g., PMT dark counts, accidental coincidences). * Integrate machine learning techniques (e.g., boosted decision trees) to improve signal identification and background rejection. * Collaborate with theorists to refine predictions of ionization spectra produced by dark matter-induced nuclear recoils. ## Related Videos - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Implementing continuous delivery in a data processing pipeline](https://www.wearedevelopers.com/videos/73-implementing-continuous-delivery-in-a-data-processing-pipeline) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) ## Related Articles - [Average Salary in France](https://www.wearedevelopers.com/magazine/269-average-salary-in-france) - [Guide for Expats Living in France](https://www.wearedevelopers.com/magazine/305-guide-for-expats-living-in-france) - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [Best Companies to Work For in France: Top 25 Companies in 2023 ](https://www.wearedevelopers.com/magazine/189-best-companies-to-work-for-in-france-top-25-companies-in-2023) - [Best Companies to Work For in Paris: Top 25 Companies in 2023 ](https://www.wearedevelopers.com/magazine/190-best-companies-to-work-for-in-paris-top-25-companies-in-2023) - [Jobs in Tech: The State of the European Market](https://www.wearedevelopers.com/magazine/575-jobs-in-tech-the-state-of-the-european-market)