Research Fellow (Machine Learning) 1

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Machine-Learning%29-1/32348-en_GB/

We regret that only shortlisted candidates will be notified.

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).