Scientist for Stochastic Parametrisation and Differentiable Physical Processes Salary and Grade: Grade A2 GBP 76,384 (UK) or EUR 91,754 (DE); Grade A3 GBP 94,251 (UK) or EUR 113,224 (DE) NET annual basic salary + other benefits Deadline for applications: 06/05/2026 Location: Reading, UK or Bonn, Germany Contract type: STF-C, Duration: Four years with the possibility of future extensions Your role We are looking for a highly motivated (Senior) Scientist to work on the representation of uncertainty in ECMWF’s ensemble forecasts of the Integrated Forecasting System (IFS) and the maintenance of the tangent linear (TL) and adjoint (AD) code for the IFS physical parametrisations used during the minimisation process of 4DVar data assimilation. The work on uncertainty representation includes the current operational stochastically perturbed parameterisations (SPP) scheme, the use of singular vectors and the uptake of initial conditions from the ensemble data assimilation system. Both the uncertainty representation and TL/AD have an impact on the quality of the world‑leading data assimilation and physical ensemble forecast system for numerical weather prediction at ECMWF. The successful candidate will also support developments of the Artificial Intelligence Forecasting System (AIFS) relevant for ensemble forecasting, providing advice on the representation of physical processes in data‑driven ensemble forecasts, helping with the generation of training datasets, and potentially working hands‑on with machine‑learned ensemble models. The work requires both technical expertise to create stable and resilient model configurations and a good understanding of the underlying physical processes and mathematical algorithms of the IFS. Your responsibilities

Enhance representations of uncertainties (e.g. the SPP stochastic parametrisation scheme) for use in numerical weather predictions across forecast lead times (from days to seasons) and for km‑scale model simulations and Digital Twins of the Earth system. Maintain and update the tangent linear (TL) and adjoint (AD) model code for the physical parametrisation schemes of the IFS, including testing and exploring new methods such as automatic differentiation and deep‑learning emulation. Support developments of the AIFS ensemble system by providing insight into the representation of physical processes and generating datasets for training data‑driven ensemble models.

What we're looking for

Excellent analytical and problem‑solving skills with a proactive approach to model and tool improvement. Excellent interpersonal and communication skills. Self‑motivated and able to work with minimal supervision, while also being dedicated to teamwork and close collaboration. Ability to maintain effective communication and documentation of scientific results. Highly organised, capable of working on a diverse range of tasks to tight deadlines.

Your profile – Education, experience, knowledge and skills

Advanced university degree (EQ7 level or above) in a physical, mathematical or environmental science, or equivalent professional experience. Experience in Earth system modelling, including code contributions and use of large simulations on modern supercomputing environments. Experience in stochastic parametrisation schemes and/or generation of tangent linear and adjoint model code handling is desirable. Expertise in atmospheric physical processes, numerical weather prediction and operational weather prediction methodology is desirable. Candidacy requires proficiency in English.

Benefits Flexible teleworking policy, 10 remote working days per month (up to 80 days per year within participating countries), and relocation support are provided. Who can apply Eligible applicants include nationals from ECMWF Member and Co‑operating States, and, in exceptional circumstances, Ukrainian nationals. Applications from other countries may be considered in exceptional cases. Equal Opportunity Statement ECMWF is dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction based on race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity, or culture. Eligible applicants are welcomed to apply. #J-18808-Ljbffr