Each year, Atmospheric, Oceanic and Planetary Physics offers summer vacation projects. These projects are open to students from any University but candidates must have an existing right to work in the UK.
Students will work with a supervisor in the Department, usually a research fellow or a faculty member, on a self-contained research project. Students are encouraged to take part in the Department’s life, joining researchers for coffee breaks, discussions and seminars.
The projects will typically run for 8 weeks, beginning on or around 1 July. The duration may be adjusted to be shorter or longer, or to accommodate summer travel. The projects are usually full-time but hours can be discussed with your supervisor. Students will be paid as employees of the University, receiving a payment of at the Oxford living wage (subject to tax and National Insurance deductions).
For administrative enquiries please contact andrea.simpson@physics.ox.ac.uk
Project(s) available in 2026:
Project 1
Title: Biases and Limits in Radiance Forward Models for Volcanic Ash
Supervisors: Antonin Knizek, Roy Grainger, Isabelle Taylor (University of Oxford); Robert Tubbs (UK Met Office)
Accurate representation of volcanic ash and sulphur dioxide (SO₂) in the atmosphere is essential for both climate studies and operational forecasting, particularly for aviation safety. Satellite radiance measurements are a key tool in detecting and quantifying these hazards, but their interpretation depends critically on forward models that simulate how radiation interacts with atmospheric constituents. Widely used models such as RTTOV provide fast, parameterised calculations, while line-by-line codes such as our in-house Scattering Reference Forward Model (SRFM) offer more detailed, line-by-line physical treatments of scattering, clouds, ash, and aerosols. Understanding the differences between these approaches is crucial for improving retrieval algorithms used in both research and operational settings.
The aim of this project is to analyse and compare two radiance datasets generated using RTTOV (at the UK Met Office, see here: https://doi.org/10.1029/2024JD041112) and SRFM (at the University of Oxford). The focus will be on how the models represent volcanic ash under a range of conditions, including varying ash properties and SO₂ concentrations. Particular attention will be given to extreme scenarios, such as high ash loadings and large optical depths, where discrepancies between models are expected to be most pronounced. By identifying systematic differences and trends in the simulated radiances, the project will aim to diagnose the underlying causes of model biases and assess the limitations of each approach.
The project will involve data analysis and comparison of model outputs, with opportunities to engage with both academic researchers and operational scientists. The work will contribute to improving forward modelling capability and, ultimately, the accuracy of satellite-based retrievals used in real-world applications. The project will be based at the University of Oxford, with collaboration and interaction with the UK Met Office, including opportunities for short research visits between the two institutions.
Funding is available to support travel between Oxford and the Met Office, with provision for several short visits during the project. The project is planned for 8 weeks and will begin on or around 1 July 2026.
Skills Required:
This project would suit a student from physics, atmospheric science, mathematics, or a related discipline. Some experience with data analysis and programming (e.g. Python, MATLAB, or similar) is desirable. An interest in atmospheric physics, remote sensing, or radiative transfer would be beneficial, but prior knowledge of volcanic ash modelling is not required.
How to Apply:
Applicants should send a CV, the name and contact details of one academic referee, and a short cover letter outlining their interest in the project to Antonin Knizek (antonin.knizek@physics.ox.ac.uk). Please feel free to get in touch for further information about the project.
Applications will be reviewed on an ongoing basis until the position is filled, with review of applications commencing on Friday 1st May 2026.
Project 2
Title: Improving rainfall prediction with generative AI: a comparison of training using satellite and radar data
Supervisory Team: Fenwick Cooper, Shruti Nath, Matthew Wright, Antje Weisheimer
Outline of work:
The Predictability Group in Atmospheric, Oceanic and Planetary Physics is looking for a motivated undergraduate summer student to work with us over the summer. The student’s work will contribute to a large international project, which aims to improve prediction of rainfall over East Africa, and provides daily publicly-available forecasts.
As part of the Strengthening Early Warning systems for Anticipatory Action (SEWAA) project, funded by the World Food Programme, we have developed a conditional Generative Adversarial Network (cGAN), which post-processes short-term weather forecasts from the European Centre for Medium-range Weather Forecasting (ECMWF). This machine learning technique is used to produce daily forecasts, disseminated to partner organisations in Africa and available online.
The cGAN model is currently trained using the IMERG satellite-derived rainfall dataset. This project will explore the benefits of training the cGAN model on radar observations, providing the first comparison between these two data sources. Using UK-based IMERG and radar datasets, the student will train the model and evaluate differences in performance.
This project would involve:
- Accessing and processing satellite and radar rainfall datasets
- Training a conditional Generative Adversarial Network (cGAN) on different data sources (we have detailed tutorials available to help with this)
- Evaluating and comparing model outputs trained on IMERG and radar data
- Analysing results to assess impacts on rainfall prediction skill
The student working on this project will gain experience in:
- Machine learning techniques, with a focus on generative models
- Data analysis and scientific computing in Python
- Atmospheric science, particularly rainfall processes and forecasting
- Working in an interdisciplinary research environment
- Engaging with academic research through seminars and group discussions within our group and the wider department
Desired candidate characteristics:
- Undergraduate student in Physics, Maths, Engineering, Environmental Sciences, or a related subject
- Enthusiasm for weather and climate science, and for applying that knowledge to other sectors
- Ability to analyse data and present results clearly and concisely
- Some experience of programming, ideally using Python (although lots can be learned during the placement, some experience is ideal)
Even if you don’t meet all the characteristics but are still interested in the project and think you would be a good fit, please do apply!
This will be a paid position, at the university’s standard summer student rate.
The start date and duration are flexible, but we envisage the student starting towards the end of June, and working with us for 6-8 weeks. The student will ideally be based in Oxford for the duration of the project, to facilitate in-person collaboration, but we are open to discussing hybrid arrangements.
To apply:
Please send a one-page CV and summary of your motivation for applying (maximum 250 words) to matthew.wright@physics.ox.ac.uk. We will keep applications open until a suitable candidate is found.