Address
Form of work
SalaryOverview of Responsibilities
UKAEA’s pay arrangements and grading structure are currently under review as part of a transformation project and proposed multi-year pay deal. UKAEA aims to introduce a new grading structure in 2024. This will enable us to lead the delivery of sustainable fusion energy and maximise the scientific and economic benefit. Now is a great time to join the Organisation and be part of the journey.
This role requires employees to complete an online Baseline Personnel Security Standard (BPSS) , including The Disclosure & Barring Service (DBS) checks for criminal convictions.
The Role
Are you looking for an exciting opportunity to make a difference? Join our team and contribute to the future of fusion energy.
The Computing Division at UKAEA plays a vital role in fusion reactor research, covering HPC, data solutions, algorithm development, and AI. This role, within the plasma simulation group, applies modern computational methods to areas of plasma physics. We collaborate closely with specialists in the plasma division and are aiming to expand partnerships with institutions in the US and Europe. Key research areas include developing new simulation capabilities, utilizing machine learning for reactor design, and deploying uncertainty quantification tools. Candidates need a background in plasma physics or a closely related discipline, plus experience in scientific computing and/or knowledge of machine learning. The roles may involve the opportunity for an extended secondment to a partner institution.
Responsibilities:
- Lead and contribute to new code development under the group leader’s direction, conducting detailed code analysis and performance evaluations.
- Conduct research into novel numerical techniques applicable to fusion and related fields, managing the migration of codes to new computing architectures and evaluating their effectiveness.
- Develop innovative techniques at the intersection of HPC and AI/ML for exascale computing, ensuring scalable training, deployment, and evaluation of machine learning models.
- Collaborate with national and international partners to optimize codes for peta- and exa-scale systems, evaluate novel computing architectures, and lead efforts in performance portability to drive future advancements.
