The Machine Learning (AppStat) group of the Linear Accelerator Laboratory (LAL) is seeking a postdoctoral researcher for working on machine learning motivated by experimental physics. The position is financed by the ANR Siminole project (http://siminole.lal.in2p3.fr). Some of the ongoing themes are large scale MCMC in hierarchical parametric models, budgeted learning for real-time triggers, and unsupervised (deep) feature learning for next-generation high-resolution pixel calorimeters. 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., Auger, LHCb@CERN, the future ILC or JEM EUSO). The ideal candidate should have a recently completed Ph.D. in the areas of machine learning or computational statistics, and an open spirit to work with researchers of different disciplines.
AppStat (http://appstat.lal.in2p3.fr) is an interdisciplinary research group with the mission of creating a scientific link between experimental physics and machine learning. AppStat is part of the Linear Accelerator Laboratory (LAL) and it also has strong ties to the Machine Learning and Optimization team (http://tao.lri.fr) of the Computer Science Laboratory (LRI). Both laboratories are part of the University of Paris-Sud campus, located in the outskirts of Paris. The position is available for a period of two years starting in February, 2013. The monthly salary is in the 2500-3000 euro range depending on experience. Interested candidates should send a cover letter, a curriculum vitae, and the names and addresses of three referees before December 20, 2012 to Dr. Balázs Kégl (firstname.lastname@example.org), and should be ready for an interview in the beginning of January.
PS: I will be at NIPS, don’t hesitate to contact me if you would like to discuss the position.