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Research Engineer/Assistant (Agentic AI & Urban Intelligence)-Cities Foresight Lab(CFL),NUS Cities

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National University of Singapore (NUS)

Kent Ridge Campus

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Research Engineer/Assistant (Agentic AI & Urban Intelligence)-Cities Foresight Lab(CFL),NUS Cities

Research Staff

2026-06-29

Location

Kent Ridge Campus

National University of Singapore (NUS)

Type

Full-time Research Staff

Required Qualifications

Python Proficiency
Agentic Frameworks (LangGraph)
MCP Integration
Data Engineering
NLP Tasks
Bachelor's/Master's in CS/AI/Data Science

Research Areas

Agentic AI
Urban Intelligence
Spatiotemporal Data
Policy Planning Workflows
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Research Engineer/Assistant (Agentic AI & Urban Intelligence)-Cities Foresight Lab(CFL),NUS Cities

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Architecture

Employee Category: Research Staff

Location: Kent Ridge Campus

Posting Start Date: 28/04/2026

Job Description

NUS Cities Foresight Lab (CFL) is seeking a Research Engineer/Assistant to develop an Agentic Orchestration Framework for Urban Intelligence. The project aims to demonstrate how agentic systems can enhance efficiency and consistency in policy and planning workflows by synthesizing unstructured datasets (e.g., text, social media, regulatory frameworks) with structured spatiotemporal information to generate responsive, data-driven recommendations.

You will be responsible for implementing a scalable Agentic Framework with Model Context Protocol (MCP) integration to connect diverse data sources with expert agent models. The role involves developing an orchestration layer capable of transforming complex, multi-scale technical inputs into actionable, reasoned insights through a natural-language interface. You will work alongside urban planning and social science researchers to ensure the system's outputs are interpretable, context-aware, and relevant to real-world policy and planning workflows.

Key Responsibilities

  • Agentic Framework Design: Develop and maintain a multi-step reasoning framework (e.g., LangGraph or similar) that can autonomously decompose high-level user objectives into executable tasks.
  • MCP Integration: Implement and scale Model Context Protocol (MCP) servers to standardize the interface layer between external data repositories, real-time APIs, and specialized analytical models.
  • Urban Intelligence Case Demonstration: Perform data synthesis for case studies, spatiotemporal tool engineering, and expert agent tuning.
  • Performance Evaluation: Define and track system performance (e.g., API compatibility across system architecture, step tracing) and user validation metrics (e.g., ground-truthing, benchmarking against manual workflows).
  • Documentation & Publication: Contribute to data/method documentation, visualisations, and writing reports/publications.

Qualifications

Qualifications
• Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field with a strong computational focus.
• Proficiency in Python.
• Hands-on experience with agentic frameworks, specifically in designing multi-step reasoning loops and tool-calling logic, and system architecture, including MCP.
• Experience in data engineering, including the ability to handle both unstructured and structured datasets.
• Familiarity with natural language processing tasks such as sentiment analysis, knowledge bases, and information retrieval.
• Resourceful and critical with good communication skills; able to work independently while collaborating effectively with interdisciplinary teams of urban planners and social scientists.

Preferred
• Experience in retrieval-augmented generation (RAG), knowledge graphs, or structured reasoning over heterogeneous data sources.
• Familiarity with cloud deployment environments and modern software development practices.
• Familiarity with geospatial data analysis and libraries (e.g., GeoPandas, Shapely, or equivalent).
• Familiarity with explainability and trust frameworks for AI systems, particularly in public sector or governance contexts.
• Inter

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Frequently Asked Questions

🎓What qualifications are required for the Research Engineer/Assistant role at NUS Cities Foresight Lab?

Candidates need a Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Data Science, or related field. Key skills include Python proficiency, hands-on experience with agentic frameworks like multi-step reasoning and MCP integration, data engineering for unstructured/structured data, and NLP tasks such as sentiment analysis. Explore more on research assistant jobs or excelling as a research assistant.

🔧What are the key responsibilities in developing the Agentic Orchestration Framework?

Responsibilities include agentic framework design using tools like LangGraph for task decomposition, MCP integration for data sources and APIs, urban intelligence case studies with data synthesis and spatiotemporal tools, performance evaluation via metrics and benchmarking, and contributing to documentation and publications. Check research jobs for similar roles.

What preferred skills enhance candidacy for this Urban Intelligence position?

Preferred experience includes RAG, knowledge graphs, cloud deployment, geospatial analysis (e.g., GeoPandas), and AI explainability in governance contexts. These align with interdisciplinary work in urban planning. See tips in thriving in research roles.

📝How to apply for the NUS Research Engineer role in Agentic AI?

Applications are open until 2026-06-29. Use the Apply now link in the job post. Prepare a CV highlighting agentic AI and Python experience. Tailor for interdisciplinary teams. Resources: free resume template and winning academic CV guide.

📍What is the work environment and location for this NUS Cities Foresight Lab position?

Located at Kent Ridge Campus, this full-time research staff role involves collaboration with urban planners and social scientists. Focus on scalable agentic systems for policy workflows. No teaching load. View similar research assistant opportunities.

🧠What research focus does the Agentic AI Urban Intelligence project emphasize?

The project develops an Agentic Orchestration Framework integrating MCP for synthesizing unstructured data (text, social media) with spatiotemporal info for data-driven urban policy recommendations. Outputs must be interpretable for real-world planning. Related: AI leadership courses.

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