STFC
Organisation STFC
Organisation Detail Science and Technology Facilities Council
Reference Number IRC255076
Location Didcot See on Map
Salary £31305 - £43300
Date Posted 10 September 2020
Grade UKRI-Band E
Contract Type Fixed Term Appointment
Hours Full Time
Closing Date 23 October 2020
Interview Date




Brief Description


Salary: £31,305-£34,028 (Band D) and £38,969-£43,300 (Band E)
Grade: D/E
Contract Type: Fixed Term (3 Years)
Hours: Full Time
Closing Date: 23rd October 2020
Interview Date: W/c 2nd November 2020

About Us

At the Science and Technology Facilities Council (STFC), one of Europe’s largest multidisciplinary research organisations, the expertise of our computing staff is key to making our research happen. Consequently, we are committed to developing our staff, and training will be provided in relevant areas.  We work with the very latest technologies to drive advances in both hardware and software that have genuine real-world applications. Whether it is the search for the Higgs Boson and dark matter, analysing climate data or genomics, our systems tackle the biggest and most challenging problems in scientific computing.

STFC’s Scientific Computing Department (SCD) develops leading edge software, compute, and data storage infrastructures, to support the work of world class science both within STFC and internationally.

As part of UK Research and Innovation, STFC offers a working environment and benefits package designed to provide an excellent work/life balance. For this opportunity, we welcome applications on a full-time, part-time (minimum 25 hours) or term-time only basis and we also offer a flexible working scheme. Further benefits include 30 days’ (pro rata) annual leave, 10.5 public and privilege days, Christmas shut down, a workplace nursery, an exceptional defined benefit pension scheme, and social and sporting activities and societies. STFC is an open and inclusive work environment, committed to promoting equality, diversity and inclusion.

Background

The Harwell campus of the Science and Technologies Facilities Council is home to the Rutherford Appleton Laboratory (RAL) and to the UK research community’s large-scale experimental Facilities. These include the Diamond Synchrotron and Electron Microscopy facilities, the ISIS Neutron and Muon Facility, the Central Laser Facility, and Centre for Environmental Data Analysis (CEDA). Researchers from universities and from industry use these facilities for a very wide range of scientific applications ranging from revealing ancient fossils and improving battery technology, to characterising materials to understanding the impact of climate change.

Following recent progress in cryogenic electron microscopy (cryoEM), it is now possible to determine structures of biological macromolecules in atomic detail from images of biomolecules in frozen solution. This revolution has brought an explosion of interest in structure determination by cryoEM, in addition to the established methods of X-ray crystallography and NMR, to aid our understanding of the fundamental mechanisms of life as well as a playing a key role in novel drug discovery.

Historically both cryoEM and X-ray crystallography have focused on resolving static structures of biomolecules although they in fact highly dynamic entities.  Moreover, these motions are implicit to protein function and elucidating these movements would provide a step-change in the understanding and regulation of proteins.  Here there is vast potential to apply state-of-the-art machine learning algorithms to improve data-processing to answer this vital question which has challenged the field of structural biology since its inception.

This post is one of the four interleaved positions connecting the Alan Turing Institute, STFC, University College London (UCL), Medical Research Council, Laboratory of Molecular Biology (MRC-LMB) and the University of Cambridge, who will address this challenge together.  This STFC position in the Scientific Machine Learning (SciML) and CCP-EM (Collaborative Computational Project for Electron cryo-Microscopy) which are co-located at the Rutherford Appleton Laboratory (RAL) in Oxfordshire, alongside the Electron Bio-Imaging Centre (eBIC).

SciML works closely with the large-scale experimental facilities at RAL, and their users, in applying and developing state-of-the art AI and machine learning methods to translate their data into innovative new science. The SciML Group is also a ‘Turing Hub’ – a component of the Alan Turing Institute’s ‘AI for Science’ initiative.  CCP-EM (Collaborative Computational Project for Electron cryo-Microscopy) supports users and developers in computational cryoEM, and develops its own software suite for managing cryoEM datasets.

Duties & Responsibilities:

This post will leverage the skills and experience in the SciML and CCP-EM groups to produce to create a rich metadata pipeline which will allow datasets to be achieved effectively to provide rich training data.  This will empower the development of next-generation ML algorithms for this project, through the collaborations at the partner sites, as well as for other developers in the cryoEM community.  Furthermore, the post will use the metadata collected to learn optimal processing pathways to guide new users and improve automated pipelines.  Finally, this post will also explore how the dynamic reconstructions can be applied to atomic models using steered molecular dynamics.

Specific responsibilities include:

  • Develop ML or relevant techniques to understand, interpret and extract features from experimental datasets
  • Implement these techniques in a commonly used programming language, like Python
  • Gather annotated datasets that can be used to develop future ML algorithms
  • Contributing to learning and development at the Rutherford Appleton Lab, and supporting its community by:
    • Presenting work internally and externally
    • Publishing work in peer-reviewed journals
    • Helping researchers to understand the power and limitations of Machine Learning technologies applied to their real-world data
    • Assisting in running training courses and providing consultancy to both university and industrial users.

Contacts and Communication

  • Regular contact with staff internally and with the collaborators across Cambridge, LMB, UCL and Alan Turing Institute sites
  • Assist in organising and coordinating technical meetings and relevant project events as appropriate
  • Maintain effective engagement with stakeholders and collaborators.

Personal Skills and Attributes

  • Strong communication skills (verbal, written, and presentation)
  • Ability to work effectively under pressure, set priorities and make decisions independently
  • Ability to work with uncertain requirements to develop a consensus on a plan of action
  • High level of self-motivation and drive
  • The ability to work as a team member delivering commitments on time
  • A proven track record of analytical and problem-solving skills.

Further information:

http://www.scd.stfc.ac.uk/ (Scientific Computing Department)

https://www.scd.stfc.ac.uk/Pages/Scientific-Machine-Learning.aspx (Scientific Machine Learning group)

https://www.ccpem.ac.uk/ (Collaborative Computational Project for Electron cryo-Microscopy)

For further information about this position please contact Jeyan Thiyagalingam (t.jeyan@stfc.ac.uk) or Tom Burnley tom.burnley@stfc.ac.uk.


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 and Interview Criteria

Both Band D and E will have the following essential and desirable criteria

Essential:

  • PhD in relevant scientific or computer science discipline or equivalent experience (S)
  • Awareness of software engineering principles, with regard to robustness, portability and usability (I)
  • Familiarity with one or more machine learning toolsets (e.g. SciKit Learn, TensorFlow, PyTorch, etc.) (S&I)
  • Evidence of collaboration during software development or scientific research (S&I)
  • Evidence of strong scientific communication skills (verbal, written, and presentation) (S&I)
  • Ability to work both as part of a team, and with a high degree of autonomy (I)
  • Able to travel in the UK and occasionally abroad (I).

Desirable:

  • Experience in structural biology or bioinformatics (S/I)
  • Experience in cryoEM method development or application (S/I)
  • Evidence of algorithm development for scientific research (S/I)
  • Experience with high performance computing (S/I).

In addition to these, applicants for the Band E position must meet the following essential criteria:

  • A PhD or equivalent professional experience in a field with relevance to data science / cryoEM (S)
  • Deep understanding of managing, structuring, and analysing data, including building statistical models and using machine learning technologies (S&I)
  • Strong leadership skills with previous experience of/the ability to lead, motivate and develop others (S&I).

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.

Polaris House is located next to Swindon Train Station and has excellent public transport links. Limited parking subject to waiting list.

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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