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Science and Technology Facilities Council (STFC)
Daresbury, Warrington, UK
£25,198 - £37,028 per annum
8 November 2020
STFC’s Accelerator Science and Technology Centre (ASTeC) undertakes particle accelerator research for current and future ‘Big Science’ facilities. We work on numerous high-profile projects with other STFC departments, national partners such as Diamond, and with international labs such as CERN, DESY and PSI. ASTeC has experience in designing, commissioning and operating particle accelerators, with a focal point for these efforts being a dedicated accelerator test facility – CLARA, hosted at Daresbury Laboratory. CLARA is being built in stages to test concepts particularly relevant for novel acceleration and Free-Electron Lasers (FELs) – providing a platform for underpinning R&D for a future UK X-ray FEL, while being broadly relevant for a variety of projects. ASTeC is a key partner within the world leading Cockcroft Institute (CI). We work closely together on a broad range of accelerator research topics and have a number of joint PhD studentships with CI university partners.
Accelerator facilities are major investments in research infrastructure, used to investigate many of society’s most pressing problems, so it is vital that they be utilised as effectively as possible. A key ambition for the next generation of facilities is to harness the vast amounts of data available in order to deliver rapid optimisation and control. Obtaining the ultimate accelerator performance will require a research and development programme incorporating online and offline data with advanced control methods such as non-linear optimisation algorithms, machine learning, and artificial intelligence. Many of these techniques are still in their infancy and so we are planning to expand in this area. Therefore, many opportunities exist for a well-motivated candidate to work with the team of scientists, engineers and academic collaborators in developing new computational and data science systems whilst also helping to build the next generation of world-class ‘Big Science’ particle accelerators.
The role is to be ASTeC’s machine learning specialist, working to develop and implement machine learning techniques for particle accelerators. We are therefore looking for a person with machine learning training/experience who is excited by the challenge of applying their expertise in this area. We welcome applicants at both first degree level and PhD level, with recruitment to either Band C/D respectively. The ideal candidate shares our passion for applying programming and problem solving to science and technology. The role will sit within the Magnetics and Radiations Sources group, with strong connections to the Accelerator Physics group in particular but with broad connections with the various machine learning interests throughout the department. There will also be connections to the SciML expert group within the Scientific Computing Department and national and international networks, including specialist machine learning training opportunities. The successful candidate would in turn be expected to help disseminate best practices within ASTeC. The CI education and training programme targeted at post graduate level covers all aspects of particle accelerators.
Key duties will include the application of machine learning techniques to both online and offline systems. Machine learning techniques will be incorporated into advanced accelerator controls software to optimize performance during operation. Offline studies with datasets generated from the machine or simulations will underpin the online applications. Key techniques are anticipated to include data classification, clustering, dimensionality reduction, and image recognition.
You will have a strong background in a quantitative field (physics, maths, computer science, statistics, or similar) with good programming skills. You should have a solid understanding of the skills & techniques needed to work with modelling physics systems and experience with machine learning and data science techniques.
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