Full Time/Part Time
This post is one of the four interleaved positions connecting the Alan Turing Institute, The Science and Technology Facilities Council (STFC), University College London (UCL), Medical Research Council, Laboratory of Molecular Biology (MRC-LMB) and the University of Cambridge.
This position will incorporate 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).
The Harwell campus 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.
About the role
Within this post, you will work very closely with the operational and technological teams of large-scale facilities, such as accelerator, beamline, detector and control teams, to ensure that AI solutions can be deployed within the compute units of the facilities. These compute units are considered to be “edge-devices”, and, as such, often limited in computational resources. It is in addition to developing / understanding state-of-the-art machine learning (ML) techniques for analysing operational datasets from facilities and detectors, you will also be contributing towards group’s core programme on benchmarking, and “AI at the Edge (or Edge AI)”.
- Developing ML or relevant techniques to transform datasets from detectors and facilities into a form that can be consumed by ML frameworks to be developed in the project.
- Developing ML techniques to understand, interpret and extract features from these datasets
- Developing techniques to combine & fuse multiple data sources for better exploitation of information
- Working closely with the SciML team members, accelerator, beamline, detector and control teams to develop and suitable solutions,
- Deploying AI solutions at the edge
- Contributing to learning and development at the Rutherford Appleton Lab, supporting its community by presenting work internally and externally, and by publishing work in peer-reviewed journals.
To be successful in the role you will be educated to PhD level in relevant scientific or computer science discipline (or will have equivalent experience).
You’ll also be expected to have experience in a runtime efficient programming language (e.g. such as C/C++)
You will be able to demonstrate evidence of familiarity with one or more machine learning toolsets (e.g. SciKit Learn, TensorFlow, PyTorch, etc.)
As part of UK Research and Innovation, STFC offers a benefits package designed to provide an excellent work/life balance. This includes 30 days’ annual leave, 10.5 public and privilege days, Christmas shut down, flexible working hours, an exceptional defined benefit pension scheme, and social and sporting activities and societies.
Applicants are required to include a cover letter outlining their suitability for this role.
Applications are handled by UK SBS; to apply please click the apply button. Applicants who are unable to apply online should contact us by telephone on +44 (0)1793 867000.
The closing date for applications is 17th February 2021.
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