Research Assistant (Occupant-Centric Controls)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 20,000 - 60,000; Full time

Job summary

A leading academic institution in Singapore is seeking a highly motivated Research Assistant to support a research project on occupant-centric strategies for building energy retrofit. The successful candidate will design control strategies, develop workflows using large language models (LLMs), validate methods, and perform quantitative analysis. A Bachelor’s and Master’s degree in a relevant field and proficiency in Python are required. This role emphasizes collaboration and contribution to technical writing.

Qualifications

• Bachelor’s and Master’s degree required in a relevant field.
• Proficiency in Python for data analysis and model development is essential.
• Experience with large language models (LLMs) is necessary.

Responsibilities

• Design and evaluate occupant-centric control strategies for energy-efficient building retrofit.
• Develop LLM-based workflows for building operation data interpretation.
• Conduct systematic validation of proposed methods.
• Perform quantitative analysis of energy and operational performance.
• Support the development of reproducible modelling and analysis pipelines.
• Contribute to technical reports and peer-reviewed publications.
• Collaborate with interdisciplinary researchers.

Skills

Python for data analysis

Large language models (LLMs) experience

Communication skills

Building energy management systems

Education

Bachelor’s and Master’s degree in Architecture, Architecture Engineering, Mechanical Engineering, or related field

Job description

Interested applicants are invited to apply directly at the NUS Career Portal.

Your application will be processed only if you apply via NUS Career Portal.

We regret that only shortlisted candidates will be notified.

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