Research Assistant (IDS)

Overview

We are looking to recruit a Research Assistant for the project “LU-STM: Large Urban Spatial Temporal Model for Urban Planning and Governance”, which will be hosted at the Institute of Data Science (IDS), National University of Singapore (NUS) and led by Prof Ng See Kiong. This project aims to orchestrate end-to-end analytical workflows by using AI foundation models, and demonstrating how workflows extend human capabilities by uncovering patterns, simulating scenarios, and synthesizing information across spatial, temporal, and semantic dimensions.

Only shortlisted candidates will be notified. Please include links to your GitHub repositories showcasing your best project relevant to these topics in your CV/cover letters.

Job Description

Job Summary: The Research Assistant will support research and engineering activities for developing analytical workflows by using AI foundation models, and demonstrating how workflows extend human capabilities by uncovering patterns, simulating scenarios, and synthesizing information across spatial, temporal, and semantic dimensions. You will help design, implement, and evaluate analytical workflows; build and maintain software prototypes and experimental testbeds; and assist with data, documentation, and stakeholder engagement. This role provides hands-on experience across AI research and practical deployment at IDS.

Responsibilities:

• Design and write robust, readable, and reusable code components and applications to implement state-of-the-art research outcomes in machine learning, artificial intelligence, and big data.
• Perform data engineering tasks including data cleansing and processing for analysis of real-world datasets.
• Assists with the editing and preparation of manuscripts, reports and presentations.
• Participate in presentations and demos for exhibiting work at appropriate events.

Requirements

• Bachelors or Masters in Computer Science with a focus in AI, Machine Learning and Big Data.
• Solid programming and application development skills (Python preferred) and experience with ML frameworks (e.g., PyTorch, TensorFlow) and modern development practices (Git, testing, CI/CD).
• Ability to develop robust systems and prototypes with fast turn-around.
• Possesses research background with ability to read and understand methodologies in research papers.
• Fluent in English and good team-player.
• Prior AI expertise with knowledge and interest in spatio-temporal foundation models and urban data analytics is preferred.