Postdoctoral Researcher in Machine Learning for Exoplanet Atmospheric Modelling (Milton Keynes, ENG, GB, MK7 6AA)

Salary: £

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About the Role

The School of Physical Sciences at the Open University, UK, invites applications for a 3-year fixed-term postdoctoral researcher in machine learning for exoplanet atmosphere modelling. The postdoctoral researcher will work with Dr Joanna Barstow and Dr Hugh Dickinson as part of the STFC-funded research project 'Rainbow Connection: Exoplanet Cloud Scattering Via Neural Networks'. The role will involve developing a neural network-based emulator for Mie scattering calculations, integrating the emulator into the NemesisPy atmospheric model, and applying this to observational data from the James Webb Space Telescope to constrain hot Jupiter cloud composition.

Key Responsibilities

The person appointed to this post will undertake duties to include:

1. Developing, training and testing a neural network Mie scattering emulator. 2. Using existing Mie scattering routines to construct training and test data sets. 3. Modelling exoplanet transmission spectra and comparing to observational data; duties may also include writing telescope proposals to obtain further data. 4. Leading scientific publications related to the research outcomes. 5. Working with, and providing day to day support to, PhD students in exoplanet atmospheres. 6. Disseminating the research at major national and international conferences. 7. Developing their own independence by leading observing proposals, and leading and/or co-ordinating work within international teams.

All Staff are expected to:

- Co-operate with the Open University in ensuring as far as is necessary, that Statutory Requirements, Codes of Practice, University Policies and Departmental Health and Safety arrangements are complied with. - Ability to carry out the role in a way that is consistent with equality legislation and University policies. - Attend /complete appropriate staff development events / courses.

About You

Essential:

- PhD in Astronomy, Astrophysics or a related field. - Experience in modelling exoplanet atmospheres OR experience in applying machine learning techniques, especially neural networks, to astrophysical data. - A developing track record of peer-reviewed publications in international journals. - Experience of Python programming for scientific data processing and analysis. - Time management and project planning skills. - The ability to present your research effectively both orally and in scientific writing. - The ability to work both independently and as part of a diverse team.

Desirable:

- Experience in spectral retrieval of exoplanet atmospheres. - Experience working with JWST observations of exoplanets.

Support with your application

If you have any questions, or need support or adjustments relating to your application, the recruitment process, or the role, please contact us on or email quoting the advert reference number.

What's in it for you?

At The Open University, we offer a range of benefits to recognise and reward great work, alongside policies and flexible working that contribute towards a great work life balance. Get all the details of what benefits we offer by visiting our Staff Benefits page (clicking this link will open a new window).

Flexible working

We are open to discussions about flexible working. Whether it’s a job share, part time, compressed hours or another working arrangement. Please reach out to us to discuss what works best for you.

Work location

It is anticipated that a hybrid working pattern can be adopted for this role, where the successful candidate can work from home and the office. However, as this role is contractually aligned to our Milton Keynes office it is expected that some attendance in the office will be required when necessary and in response to business needs.

Next steps in the Recruitment process

If shortlisted, we anticipate interviews taking place on either 20th or 21st May.

How to apply

To apply for this role please submit the following document(s):

- CV - Supporting Statement (Your Supporting Statement should be no more than 1000 words and should outline how your skills and experience meet the essential and desirable criteria listed above)

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