Research Fellow in Machine Learning for Neutrino Physics
Role details
Job location
Tech stack
Job description
We are looking for an outstanding particle physicist and expert machine-learning practitioner to join UCL's neutrino group leading the development, calibration and application of state-of-the-art ML techniques for fast physics simulation of neutrino events produced in the NuMI long baseline experiments based on existing simulation and data. To extract the maximum possible information from the very basic raw data avoiding a heavy and potentially inefficient analysis chain. The goal is to improve analyses of the neutrino oscillation parameters using up to date theoretical information and potentially feed back to the theory community on parameter values.
Requirements
Do you have experience in Machine learning?, The successful candidate will have (or be about to submit) a PhD in particle physics, and a strong record of originality in research, demonstrated by a good publication record, commensurate with the applicant's career stage. They will have excellent written and verbal communication skills, and will have shown evidence of future leadership potential. Given the short-term nature of this role, experience in developing and applying cutting-edge machine learning models for applications in experimental HEP and beyond is essential.