Research Assistant (Occupant-Centric Controls)

National University of Singapore; Singapore; On-site; SGD 30,000 - 45,000; Full time

Job summary

A leading research university in Singapore is seeking a Research Assistant to support a project focused on occupant-centric control strategies for energy-efficient building retrofits. The role requires a Bachelor's and Master's degree in relevant fields, proficiency in Python, and experience with large language models. Responsibilities include designing control strategies, developing workflows for data interpretation, and conducting validation of methods. This position offers a unique opportunity to work at the intersection of AI and building performance.

Qualifications

• Bachelor's and Master's degree in Architecture, Architecture Engineering, Mechanical Engineering, or a related field.
• Demonstrated proficiency in Python for data analysis and model development.
• Experience working with large language models (LLMs), including prompt engineering and fine-tuning.

Responsibilities

• Design and evaluate occupant-centric control strategies for energy-efficient building retrofit.
• Develop LLM-based workflows for interpreting building operation data.
• Conduct systematic validation of proposed methods using simulation results.

Skills

Proficiency in Python for data analysis and model development

Experience with large language models (LLMs)

Good written and verbal communication skills

Education

Bachelor's and Master's degree in related fields

Tools

Python

Job description

Company description:

The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job Description

We are seeking a highly motivated Research Assistant to support a funded research project on cost‑effective, wireless, occupant‑centric control strategies for whole‑building energy retrofit. The project focuses on integrating data‑driven methods and large language models (LLMs) with building performance data to enable scalable, occupant‑aware control strategies for existing buildings. The role emphasizes AI‑enabled analysis, model development, and decision support, rather than traditional rule‑based control design. The Research Assistant will be supervised by Dr. Adrian Chong, Department of the Built Environment, College of Design and Engineering, National University of Singapore.

• Design and evaluate occupant‑centric control strategies for energy‑efficient building retrofit using data‑driven and AI‑enabled approaches.
• Develop and fine‑tune large language model (LLM)-based workflows to support interpretation of building operation data and control decision‑making.
• Conduct systematic validation of proposed methods using simulation results, measured building data, or benchmark datasets.
• Perform quantitative analysis of energy, comfort, and operational performance under different control and retrofit scenarios.
• Support the development of reproducible modelling and analysis pipelines, including documentation and version control.
• Contribute to the preparation of technical reports and peer‑reviewed publications, including method description, validation, and discussion of limitations.
• Collaborate with interdisciplinary researchers to integrate building performance knowledge with AI and control methodologies.
• Perform other duties as assigned.

Job Requirements

Qualifications and Skills:

• Bachelor's and Master's degree in Architecture, Architecture Engineering, Mechanical Engineering, or a related field
• Demonstrated proficiency in Python for data analysis and model development
• Experience working with large language models (LLMs), including prompt engineering, fine tuning, and integration of LLMs into decision-support workflows
• Familiarity with building energy management systems
• Good written and verbal communication skills

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department: The Built Environment

Employee Referral Eligible: No

Job requisition ID: 31475