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).