Research Felows (Postdoctoral Scholars)
OpenWe are recruiting full-time Research Felows to develop hybrid physics-AI for weather applications.
- Location: Singapore
- Type: Full-time
- Deadline: Open until filled
The Team
MathEXLab is an interdisciplinary research lab at the National University of Singapore (NUS), led by Assistant Professor Gianmarco Mengaldo. We develop next-generation mathematical modelling and AI methods for understanding and predicting complex systems, with applications in weather and climate, fluid mechanics, robotics, and socio-technical systems.
What you will do
- 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.
Skills & qualifications required / strongly preferred
- PhD in Computer Science, Data Science, Engineering, Physics, or a related field.
- Strong Python and PyTorch experience; experience with multi-GPU/distributed training and performance optimization.
- Experience with real-world geospatial/sensor data, including quality control, cleaning, and visualization.
- Strong communication and collaboration skills.
- Deep learning expertise in generative models, physics-aware learning, or uncertainty modelling is highly desirable.
- Experience with dense spatiotemporal prediction, such as video prediction or precipitation nowcasting, is highly desirable.
- Atmospheric science or tropical meteorology background is a plus, but not required.
Contract
Initial appointment: 1-year contract, renewable.
Compensation
Competitive salary commensurate with qualifications and experience.
How to apply / contact
- Assistant Professor Gianmarco Mengaldo | Email: mpegim@nus.edu.sg.
- Please include: CV, a short statement of research interests and fit, and 1–3 representative papers or a code repository link.