Postdoctoral Research Assistant in uncertainty quantification for Earth-system prediction

Corehr; Oxford; Hybrid; GBP 34,000 - 42,000; Full time

Benefits offered by this job

Teaching opportunity

Job summary

University of Oxford seeks a Postdoctoral Research Assistant in uncertainty quantification for Earth-system prediction to join the Atmospheric, Oceanic and Planetary Physics Department. The role focuses on developing methods to quantify and reduce uncertainty across short-range weather to long-term climate predictions, combining ML and physics-based approaches.

The successful candidate will work with Principal Investigator Christensen, have strong UNIX/Linux and programming skills, and may

Qualifications

• PhD/DPhil in physics, climate science, computer science or closely related discipline.
• Strong computational skills including UNIX/Linux and programming in Fortran, Python or similar.
• Ability to conduct original research of international standing.

Responsibilities

• Develop approaches to understand, quantify, and reduce model uncertainty in Earth-system prediction.
• Collaborate with PI Christensen and project team to deliver probabilistic weather and climate predictions.
• Contribute to teaching as part of the post.

Skills

UNIX/Linux

Fortran

Python

Computational skills

Education

PhD/DPhil in physics, climate science, computer science

Job description

UK date and time: 10-September-2026 23:25

Postdoctoral Research Assistant in uncertainty quantification for Earth-system prediction

Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, with some flexibility regarding hybrid/remote working patterns.

Weather and climate prediction are undergoing a profound transformation. Alongside traditional physics-based forecast systems, machine-learned (ML) weather prediction models, hybrid ML-physics models, and climate emulators are rapidly becoming an integral part of the prediction landscape. While these approaches can achieve remarkable predictive skill, fundamental questions remain regarding the quantification and representation of uncertainty within and between these models. Addressing these questions is essential if their predictions are to be used for scientific understanding and societal decision-making.

The successful candidate will join Principal Investigator Christensen in addressing the long-standing challenge of delivering reliable probabilistic weather and climate predictions. The project will develop innovative approaches to understand, quantify, and ultimately reduce model uncertainty in next-generation Earth-system prediction systems.

The research adopts a seamless perspective in two key ways. First, it will use insights gained from short-range weather forecasting to improve our understanding of longer-term climate behaviour, and vice versa. Second, it will exploit advances in machine learning (ML) weather and climate models to improve physics-based models, while using physical understanding to enhance ML approaches. By integrating ideas from numerical weather prediction, climate modelling, and modern machine learning, the project aims to develop physically grounded methods for uncertainty quantification that are applicable across the full spectrum of Earth-system prediction. The post-holder will also have the opportunity to contribute to teaching.

Applicants should hold, or be close to completing, a PhD/DPhil in physics, climate science, computer science, or a closely related discipline. They should have strong computational skills, including experience with UNIX/Linux and programming in Fortran, Python, or other high-level languages. Candidates should also demonstrate the ability to conduct original research of international standing.

The appointment is full-time and fixed-term for 24 months.

The closing date for applications is 12:00 noon (GMT) on Friday, 25 September 2026.

Interviews will take place in October 2026.

You will be required to upload a CV and Supporting Statement and details of two referees as part of your online application. The Supporting Statement should include a cover letter and should also clearly describe how you meet each of the selection criteria listed in the job description. Applicants should ensure that their two referees send their letters before the application deadline, to [e-mail]