Research Fellow (Machine Learning)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Mechanical Engineering

Employee Category: Research Staff

Location: 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).

Apply now