Research Associate in Atmospheric Dynamics for High-Altitude Autonomous Platforms (SS13809 BA2 7AY

Applications are invited for an exciting and dynamic Postdoctoral Research Associate position in Atmospheric Dynamics for High-Altitude Autonomous Platforms , working with Dr Neil Hindley at the University of Bath’s Centre for Climate, Adaptation and Environment Research (CAER).

The successful candidate will work on STRAT-NAV, a £2m research project exploring how atmospheric gravity waves can be predicted, navigated and exploited to extend the flight times of high-altitude platform systems (HAPS) operating in the stratosphere.

High-altitude platform systems (HAPS) are autonomous aircraft designed to operate in the stratosphere, above commercial aviation, where they can provide long-endurance communications, sensing and monitoring capabilities. However, many current HAPS aircraft face serious power limitations at UK latitudes during winter, when solar power is at a minimum.

Our project, STRAT-NAV, will explore the opportunity to overcome these limitations by using atmospheric gravity waves as a source of lift. Just as gliders soar on waves in the lower atmosphere, stratospheric HAPS can reduce their power consumption by navigating through gravity wave updrafts, avoiding downdrafts and regions of turbulence.

Our project will combine km-scale meteorological modelling, atmospheric wave dynamics, optimal routing methods and field observations to enable HAPS platforms to predict, navigate to and soar on gravity waves, especially during winter when stratospheric gravity waves are strongest over the UK.

Delivery of STRAT-NAV will involve closely collaboration with our project partners including British Antarctic Survey , Limosaero , Sky Sight and Radical Aero .

This role forms part of the Enduring Atmospheric Platforms research programme, funded by the UK's Advanced Research and Invention Agency (ARIA) . Backed by £50m, this programme explores the opportunity space for high-altitude autonomous atmospheric platforms operating in the stratosphere as part of an effective and economical UK communications network.

The successful candidate will use high-resolution dynamical atmospheric modelling and mathematical routing algorithms to investigate how HAPS aircraft can navigate through atmospheric gravity-wave fields.

In Year 1, the candidate will determine the maximum HAPS energy saving opportunity from gravity wave soaring, using km-scale stratospheric modelling and idealised routing algorithms. They will then take part in a UK field campaign, scheduled for winter 2026/27, where we will use intensive radiosonde balloon observations to validate the waves in these meteorological model forecasts and thereby evaluate the viability of this gravity wave assisted HAPS routing technology.

In Year 2, the candidate will apply their gravity-wave forecasting and routing capability to real HAPS test flights in the field, working with Radical Aero and other HAPS developers as part of the wider ARIA programme.

In Year 3, the candidate will refine the forecasting and routing capability across multiple HAPS platforms and assess its operational and commercial viability

We are looking for a strongly self-motivated, independent and innovative researcher who enjoys solving new problems. You will be comfortable working in a small team, collaborating closely with external partners, and contributing to a fast-moving applied research programme.

This role would particularly suit someone who enjoys both technical computing and practical fieldwork. The successful candidate will be expected to contribute to intensive observational campaigns, sometimes working to tight schedules in field conditions, as well as developing and analysing numerical models and routing methods.

You will have a PhD, or equivalent experience, in Physics, Mathematics, Meteorology, Aerospace Engineering or a related discipline.

You should also have strong programming and computational skills, including ideally Python and/or MATLAB, some familiarity with Bash and UNIX-based systems and handling large datasets.

We are especially…