Research Fellow in Machine Learning for Neutrino Physics Full-time

Research Fellow in Machine Learning for Neutrino Physics

UCL is one of the world's top universities based in the heart of London. It is a modern, outward-looking institution. UCL's staff and former students have included 33 Nobel prize-winners. It is a truly international community: more than one-third of our student body – more than 50,000 strong – come from 150 countries and nearly one-third of staff are from outside the UK.

The UCL High Energy Physics (HEP) Group is one of the largest groups in the country (~100 members, including around 20 academics and 40 PhD students), with a programme covering a broad range of the HEP field, including the exploration of the energy frontier (ATLAS), Neutrinos, Dark Matter, flavour physics and phenomenology, as well as Accelerator R&D and applications such as Proton Beam Therapy. Our neutrino group has key involvements in NuMI long baseline neutrino experiments (MINOS and NOVA) and the future DUNE experiment. We have led the major 3 flavour oscillation analyses for both NuMI experiments and maintain leadership positions within the DUNE collaboration.

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 2026 with a funding end date of 31 March 2028 – 2 years.

Please contact Prof. Jennifer Thomas for further details about this post – jennifer.thomas@ucl.ac.uk

About you

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.

What we offer

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

Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find out more.

Our commitment to Equality, Diversity and Inclusion

As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce.

These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. Our department holds an Athena SWAN Silver award, in recognition of our long-term commitment and 'beacon' status in advancing gender equality.

You can read more about our commitment to Equality, Diversity and Inclusion here: https://www.ucl.ac.uk/equality-diversity-inclusion/

Available documents

• download: Research Fellow in Machine Learning for Neutrino Physics – JD.pdf