Machine Learning and Citizen Science Postdoctoral Research Associate

Submission Information
Publish Date: 
Wednesday, February 20, 2019
Archive Date: 
Wednesday, March 27, 2019
Job Summary
Job Category: 
Post-doctoral Positions and Fellowships
Institution Classification/Type: 
Large Academic
The Open University
Department Name: 
Milton Keynes
United Kingdom of Great Britain and Northern Ireland
Job Announcement Text: 

Job Title: Machine Learning and Citizen Science Postdoctoral Research Associate

Unit Name: STEM
Salary band  Ref: AC2 (£33,199 to £39,609)  (VRF) No: 15760
Based in Milton Keynes
Temporary contract: 36 months
Full Time

We are looking for a postdoctoral research associate (PDRA) in astronomy/physics to develop crowdsourcing experiments (citizen science) and machine learning.
The role is based in the School of Physical Sciences at the Open University (OU). The research fellowship is to facilitate the design of new crowdsourcing experiments for major international astronomy, astroparticle physics and physics facilities, and act as project manager for these experiments.
You will be feeding the crowdsourcing classifications into machine learning algorithms that you will develop or adapt, which will then accelerate the classifications and allow the volunteers to focus effort on more difficult edge cases.

The project is funded through the Horizon 2020 project ESCAPE (European Science Cluster of Astronomy and Particle physics ESFRI research infrastructures), and the appointee will also liaise with other members of the ESCAPE consortium.

Duties include:
• To facilitate the creation of new crowdsourcing experiments that support the major astronomy and astroparticle physics experiments (e.g. LSST, E-ELT, SKA, CTA, FAIR, CERN, HL-LHC, EGO, EST, and/or KM3NeT, and their precursors/pathfinders), e.g. through organising international workshops.
• To adapt and/or produce simulated data for testing these citizen science experiments.
• To manage the operation of these mass participation experiments and drive their science analysis.
• To design machine learning algorithms to accelerate the volunteer classifications
• To design and/or facilitate the creation of associated text and video educational and public engagement materials for the citizen science experiments.

You will have completed a PhD in in an appropriate area and have research experience in an area relevant to an ESFRI facility (European Strategy Forum on Research Infrastructures) e.g. relevant to LSST, E-ELT, SKA, CTA, FAIR, CERN, HL-LHC, EGO, EST, and/or KM3NeT, and their precursors/pathfinders.

The Ideal Candidate will have:
• Research track record in large experiments, such as citizen science experiments
• Experience in the development of machine learning algorithms
• Research interests cognate with staff in the School of Physical Sciences, e.g. in extragalactic astronomy, LSST, E-ELT, SKA.

Please include a cover letter explaining how you meet the requirements.

For informal enquiries: please email Professor Stephen Serjeant (

Closing date: 22nd March at Noon
Interview date: TBC

For detailed information and how to apply go to , call the Resourcing Hub on 01908 55544 or email on quoting the reference number 15760.

Application Deadline: 
Friday, March 22, 2019
Current Status of Position: 
No Status Given (Opted Out)
Apply to Job
Attention To: 
Resourcing Hub
01908 55544
Institution/Company Job ID or Reference Code: