Research Fellow in Machine Learning Neutrino Physics

About the role

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.

The position can start from April with a funding end date of 31 March - 2 years.

Please contact Prof. Jennifer Thomas for further details about this post -

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.

As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below:

• 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days)
• Additional 5 days’ annual leave purchase scheme
• Defined benefit career average revalued earnings pension scheme (CARE)
• Cycle to work scheme and season ticket loan
• Immigration loan
• Relocation scheme for certain posts
• On-Site nursery
• On-site gym
• Enhanced maternity, paternity and adoption pay
• Employee assistance programme:
  Staff Support Service
• Discounted medical insurance