Job Title: Research Fellow (Machine Learning) 1
University-Level Unit: College of Design and Engineering
Faculty/Department-Level Unit: Mechanical Engineering
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 06/04/2026
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).
