Postdoctoral Research Assistant in Exoplanet Detection and Characterisation

Applications are invited for a Postdoctoral Research Assistant in Exoplanet Detection and Characterisation. The post is available initially for a fixed-term duration of 2 years (with the possibility of extension for 1 year).

The project which aims to improve our understanding of the evolution of sub-Neptune exoplanets by increasing the sample of such planets with well-measured radii and masses, particularly planets which periods longer than a week or two. The TESS mission will deliver hundreds of new transiting planets in this regime, many of which will be suitable for detailed follow-up. The postholder will work with Professor Suzanne Aigrain to measure the masses of these planets using radial velocity (RV) observations, and develop strategies to minimise the impact of stellar activity, which is often the limiting factor.

The successful candidate will be expected to design, propose and perform observations, use and develop Bayesian methods to model signals induced by both planets and stellar activity in RV data, and to infer the parameters of individual planets and their underlying distribution. They will work closely with other members of Professor Aigrain’s group, whose research focuses on transit detection, and with collaborators at other institutes. They will also have opportunities to develop cross-disciplinary collaborations with other researchers interested in exoplanets under the auspices of the Oxford Network for Planets (www.oxfordplanets.uk). The postholder will have the opportunity to teach.

Applicants should hold a PhD, or be close to completion in astronomy, astrophysics or a related field and ideally a strong background in exoplanet observations and/or stellar astrophysics. Previous experience in the field of exoplanet or stellar astronomy will be an advantage. The ideal candidate will combine practical experience of exoplanet detection and characterisation using the transit and/or RV methods with a solid mathematical and statistical background and very good computational skills. They will be expected to demonstrate the ability to develop new ideas, to work in a team, to manage their time effectively and to communicate their ideas and research findings effectively, both orally and in writing.