Job Description

We are recruiting full-time Research Fellows to develop hybrid physics-AI methods for weather applications

Available data include:

•             Numerical weather prediction (NWP) model outputs

•             Weather satellite imagery

•             Radar observations

•             Lightning detection networks

•             Surface sensor observations (e.g., rainfall and wind)

The successful candidates will:

•             Develop and benchmark multimodal AI / foundation-model approaches for spatiotemporal forecasting.

•             Build reproducible AI training and evaluation pipelines, as well as uncertainty quantification strategies.

•             Work at the intersection of physics and AI, with an emphasis on geospatial computational modelling.

•             Collaborate with domain experts and (where relevant) operational stakeholders.

•             Drive scientific breakthroughs and contribute to publications and cross-institutional collaborations

Qualifications

Required / strongly preferred

•             PhD in Computer Science, Data Science, Engineering, Physics, or related.

•             Strong Python and PyTorch; experience with multi-GPU/distributed training and performance optimization.

•             Experience with real-world geospatial/sensor data (quality control, cleaning, visualization).

•             Strong communication and collaboration skills.

Highly desirable

•             Deep learning expertise: generative models, physics-aware learning, uncertainty modelling.

•             Dense spatiotemporal prediction (e.g., video prediction, precipitation nowcasting).

•             Atmospheric science / tropical meteorology background (a plus, not required).