Particle physics
At Liverpool,our work in particle physics encompasses the whole research life cycle. We develop world-leading technologies for instrumentation and build precise detectors for major experiments. We then analyse the data from those experiments, making important discoveries about the smallest elements of the universe.
Our instrumentation - alongside our development of state-of-the-art technology that underpins it - influences the design of next generation experiments. Our insight shapes the future of the subject. We address the most fundamental questions in nature, searching for new particles, forces and phenomena, to advance the boundaries of knowledge.
Eligibility
Candidates will be expected to have a PhD in a discipline relating to their fellowship proposal, and at least three years of postdoctoral experience in an academic, research and development (R&D) or policy environment.
Candidates must apply against the defined frontier focus area listed below and clearly articulate their vision in their outline research proposal.
N.B. Host departments listed in each frontier focus area are purely indicative to aid the internal handling of applications. Successful candidates will be placed in the most appropriate University department for their discipline.
Frontier focus area
AI for particle physics
- Host school: Physical Sciences
- Host department: Physics or Mathematical Sciences.
A fellowship in this research area will focus on pioneering projects that leverage AI to advance particle physics through calculations, data analysis, simulation, and time-critical software and hardware implementations.
The fellow may, for example, further the use of deep and graph neural networks, boosted decision trees, and regression models to improve theoretical predictions, detect rare phenomena, develop novel experiments, and accelerate particle physics discoveries.
They may employ physics-informed neural networks to speed up simulations and improve accuracy, for example, or develop AI approaches that run on specialised hardware and advanced architectures to enable real-time adaptive learning and high-throughput processing - transforming both event selection and the discovery potential of experiments.
How to apply
Click here to apply for this fellowship via our e-recruitment site.