Machine Learning Scientist

  • STFC
  • Warrington, UK
  • 23/10/2020
Full time Data Science Machine Learning Big Data Data Management Statistics

Job Description

OrganisationSTFC
Organisation DetailScience and Technology Facilities Council
Reference NumberIRC255259
LocationWarrington See on Map
Salary£25198 - £37028
Date Posted05 October 2020
GradeUKRI-Band D
Contract TypeOpen Ended
HoursFull Time
Closing Date08 November 2020
Interview Date

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    • Description
    • Job Requirements
    • Additional Details
    • How to Apply

Brief Description


Salary: £25,198-£29,390 or £31,305-£37,028 (dependent on skills and experience and inclusive of annual role-based allowance)
Grade: Band C/D
Contract Type: Open-Ended
Hours: Full Time
Closing Date: 8th 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.

Organization Description


UK Research and Innovation is a new entity that brings together nine partners to create an independent organisation with a strong voice for research and innovation, and a vision to ensure the UK maintains its world-leading position in research and innovation. More information can be found at www.ukri.org.

The Science and Technology Facilities Council is a world-leading multi-disciplinary science organisation, and our goal is to deliver economic, societal, scientific and international benefits to the UK and its people – and more broadly to the world.

Shortlisting Criteria

To be considered at Band C:

Essential:

  • First degree bachelors or Masters in a relevant subject
  • Expected or have achieved a 2:1 or above
  • Experience with Machine learning techniques
  • Experience with object-oriented and/or functional programming such as C++, Python or Mathematica
  • Ability to apply knowledge of physics and/or mathematics to produce technical solutions
  • Open minded and flexible in relation to the type of work assigned with a willingness to learn new skills, both technical and non-technical.

Desirable:

  • Experience using databases such as SQL.

To be considered at Band D in addition to the above you should have:

Essential:

  • PhD in a relevant subject and/or relevant work experience applying machine learning to technical problems.

Desirable:

  • People management experience.

Interview Criteria

To be considered at Band C:

Essential:

  • Experience with Machine learning techniques
  • Experience with object-oriented and/or functional programming such as C++, Python or Mathematica
  • Ability to apply knowledge of physics and/or mathematics to produce technical solutions
  • Excellent reasoning skills and ability
  • Good communication skills and team working capabilities
  • Open minded and flexible in relation to the type of work assigned with a willingness to learn new skills, both technical and non-technical
  • Willingness to contribute to accelerator research at the highest level
  • Willingness to take part occasionally in experiment shifts on CLARA and national and international travel.

Desirable:

  • Experience using databases such as SQL
  • Good planning and time management skills
  • Ability and desire to understand context of work as part a larger whole.

To be considered at Band D in addition to the above you should have:

Essential:

  • Experience from PhD in a relevant subject and/or relevant work experience applying machine learning to technical problems.

Desirable:

  • People management experience
  • Experience in disseminating machine learning knowledge to others.

UKRI supports research in areas that include animal health, agriculture and food security, and bioscience for health which includes research on animals, genetic modification and stem cell research. Whilst you may not have direct involvement in this type of research, you should consider whether this conflicts with your personal values or beliefs.

To enable us to hire the very best people we will conduct a full and comprehensive pre-employment check as an essential part of the recruitment process on all individuals that are offered a position with UKRI. This will include a security check and an extreme organisations affiliation check.

Employee Benefits
UK Research and Innovation recognises and values employees as individuals and aims to provide a pay and reward package that motivates staff to the best of their ability. The reward and benefit package includes a flexible working scheme, an excellent Defined Benefit pension scheme, 30 days annual leave allowance and a number of other benefits.

Developing Talent
We are committed to developing employees in their roles throughout their career. Learning and development plans enable employees to continue their professional development through training and development opportunities such as e-learning, classroom training and on-the-job experiences. We encourage our employees to share their learning across teams and organisations.

Equal Opportunities
We strive to make decisions based on individual merit and ability. We welcome applications from all sections of the community and promote equality of opportunity in accordance with the Equality Act 2010. As holders of Disability Confident Employer status, we guarantee to interview all applicants with disabilities who meet the minimum criteria for the vacancy.

As this job does not fulfil the Home Office Code of Practice criterion for obtaining sponsored migrant worker status we will be unable to apply for sponsorship for anyone not eligible to work in the UK. At interview, all shortlisted candidates are required to bring with them identification documents and original documents that prove they hold or can obtain the right to work in the UK. You can check your eligibility here: https://www.gov.uk/check-uk-visa/y.
Online applications only. Please submit a covering letter and CV ensuring that the IRC reference is included in the filename description of each document uploaded. Please note that failure to address the above criteria or submitted without a covering letter may result in your application not being considered.

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