Overview

We are looking to recruit a Research Fellow for the project “Urban Intelligence Integration Framework (UI²F) for CityScan Phase 2”, 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 advance urban analytics methodology through a novel urban intelligence integration framework.

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 Fellow will be responsible for undertaking in-depth research and innovation in machine learning, data science, and artificial intelligence on a novel urban intelligence integration framework with Foundational Multi-Scale Data Processing, Temporal Relationship Intelligence, and Intelligent LLM-Powered Social Simulation and Decision Support capabilities, that leads to publications in top-tier international conferences and journals, as well as real-world implementations. The role includes designing novel algorithms, building robust software systems, and collaborating with stakeholders to translate research into practical tools and workflows.

Responsibilities:

• Develop new concepts and algorithms in data science, machine learning, and artificial intelligence for urban intelligence integration.
• Ability to work in a face-paced research environment.
• Be up to date on state-of-the-art methodologies in related technical fields and application domains.
• Develop ideas for application of research outcomes.
• Contribute to knowledge exchange activities with external partners and collaborators.

Requirements

• PhD in Computer Science, with specialization related to urban intelligence integration, machine learning, data mining or artificial intelligence.
• Proven ability to conduct independent research with a strong and relevant publication record.
• Prior AI expertise with a strong publication track record in areas such as machine learning, deep learning, reinforcement learning or LLMs/agents.
• Knowledge and demonstrable interest in urban planning with geospatial and temporal data
• Proficiency in programming and software engineering (Python preferred), including experience with ML frameworks (e.g., PyTorch, TensorFlow).
• Excellent interpersonal communication and oral presentation skills in English.