Post PhD Postdoc Position Machine Learning and Citizen Science

A fully-funded PhD fellowship in astronomy is available at Open University. Applications are welcome before March 22, 2019.

Based in Name of Region or Milton Keynes

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.

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.

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.

Interview date is to be advised.

Future rounds of recruitment will be run in the event of there being no suitable candidates.

For detailed information, call the Resourcing Hub on 01908 55544 or email on quoting the reference number 15760.

Deadline: March 22, 2019
Apply Link:

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