Research Fellow: Urban Microclimate Modeling & Graph Neural Networks

National University of Singapore; Singapore; On-site; SGD 50,000 - 70,000; Full time

Job summary

The National University of Singapore is seeking a Research Fellow specialized in Urban Microclimate Modeling and Graph Neural Networks. This role involves designing spatiotemporal modeling frameworks, developing cutting-edge GNN architectures, and executing field validations in various urban settings.

Ideal candidates will hold a PhD in a relevant field and exhibit strong programming skills in Python, along with demonstrable experience in machine learning applied to urban datasets. This position offers a unique opportunity to contribute to significant research in a collaborative team environment.

Qualifications

• PhD in Computer Science, Urban Building Science, Environmental Science, Atmospheric Science, Urban Studies, Mechanical Engineering, or a related field.
• Experience with Graph Neural Networks (GNNs), spatiotemporal deep learning, or related machine learning methods.
• Strong Python programming skills, including deep learning frameworks and geospatial analysis tools.

Responsibilities

• Design and implement a hybrid physics‑AI modeling framework.
• Develop Graph Neural Network (GNN) and Long Short‑Term Memory (LSTM) architectures.
• Execute field validation campaigns and process high-resolution satellite LST imagery.

Skills

Graph Neural Networks (GNN)

Python programming

Deep learning frameworks (PyTorch, TensorFlow)

Geospatial analysis tools (GDAL, rasterio, geopandas)

Education

PhD in Computer Science, Environmental Science, or related field

Job description

The National University of Singapore is seeking a Research Fellow specialized in Urban Microclimate Modeling and Graph Neural Networks. This role involves designing spatiotemporal modeling frameworks, developing cutting-edge GNN architectures, and executing field validations in various urban settings.

Ideal candidates will hold a PhD in a relevant field and exhibit strong programming skills in Python, along with demonstrable experience in machine learning applied to urban datasets. This position offers a unique opportunity to contribute to significant research in a collaborative team environment.