Research Associate/Senior Research Associate in Fast Machine Learning R&D

at  University of Bristol

YOU SHOULD APPLY IF

• You have (or are about to obtain) a PhD in a relevant subject area (science/engineering), or equivalent experience
• You have experience of software development in Python and C++
• You have experience using ML for scientific applications
• You have experience working with data-acquisition/trigger systems or accelerated computing

THE ROLE

The Particle Physics group of the University of Bristol (tinyurl.com/particle-bristol) UK, seeks a Research Associate or Senior Research Associate to join its machine learning R&D team. The successful applicant will be the lead developer for ML workflows on newly released FPGA-based ML -acceleration platforms, including the Xilinx Versal series. The key objectives of this project are to gain experience with these new platforms to inform potential use cases in Particle Physics and in the public sector over the next decade.
The Bristol Particle Physics group is active in development of AI/ML techniques for particle physics across a range of experiments and projects. In particular, we are developing real-time image classification and object detection for data selection at CMS and DUNE, and investigating the use of generative networks for fast detector simulation on LHCb.
Bristol is one of the top UK universities for AI research and scientific computing, and was recently chosen to host a new national AI-focussed supercomputer research facility.
The position is available until 30th October 2026.
Informal enquiries (not applications) can be made to Dr Sudarshan Paramesvaran, Sudarshan.Paramesvaran@bristol.ac.uk) or Dr Jim Brooke (jim.brooke@bristol.ac.uk)

WHAT WILL YOU BE DOING?

As a key ML researcher in the Bristol Particle Physics group you will:

• gain experience with new Machine-Learning acceleration platforms
• develop software and firmware frameworks to deploy commonly-used ML networks on these platforms
• contribute to interdisciplinary studies on the use of accelerated ML technology in other sectors e.g. health
• engage with the ECFA Detector R&D effort coordinated by CERN for future experiments, in particular as part of the DRD7 collaboration
• present work at group meetings, to collaborators, and at international conferences
• prepare results for publication