Laboratoire de l'Accélérateur Linéaire - PostDoc position in machine learning

The Machine Learning (AppStat) group of the Linear Accelerator Laboratory (LAL) is seeking a postdoctoral researcher for working at the interface of machine learning and experimental high-energy particle physics. The researcher will work with the dynamic interdisciplinary group which organized the HiggsML challenge, including Cecile Germain and Isabelle Guyon (LRI), and Balázs Kégl and David Rousseau (LAL) Some of the ongoing themes are deep learning for next-generation high-resolution pixel calorimeters, including the systematic uncertainties in training ML models for discovery, and budgeted learning for real-time triggers. All themes include the development of state-of-the-art ML solutions that can make a real difference in both the design and in the data analysis phases of ongoing and future large-scale physics experiments (e.g., ATLAS, LHCb, the future ILC). The candidate will also be part of the Paris-Saclay Center of Data Science and will be expected to participate in the mission of the center through its activities (eg, RAMPs, software carpentry, thematic days). We are accepting candidates both from data science (ML, statistics, signal processing) and from high-energy physics, given that the candidate is ready to cross the disciplinary aisles.