Research Fellow (Machine Learning)

Job Title: Research Fellow (Machine Learning)

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).