Research Associate (Resilient Urbanisation & Natural Ecosystems)

The School of Civil and Environmental Engineering (CEE) is a leading school for Sustainable Built Environment.

The School plays an integral role in spearheading tertiary education, advancing research innovations and providing professional services in a number of key disciplines in Civil and Environmental Engineering and Maritime Studies fields, with the objective of contributing to the technological and economic advancement of Singapore and beyond.

The School's mission in research is to achieve excellence by providing a conducive and intellectually stimulating environment to enable high quality work in strategic directions that are of significant impact to industry, science and technology.

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We are looking for aResearch Associate to join a CITIES OF TOMORROW (CoT) R&D project for VERTICAL 5: CITY IN NATURE. The project plays a pivotal role in ensuring a resilient urban environment against climate change and contributes to NTU strategic research in Resilient Urbanisation & Natural Ecosystems that is aligned with Singapore's national Research, Innovation and Enterprise 2025 plan.

Key Responsibilities

• Assist in planning and conducting field tests, including on-site data collection and documentation.

• Capture and process photographic data for the development of accurate 3D models using photogrammetry techniques.

• Compile, organise, and maintain experimental and field test data in structured databases.

• Support data processing and analysis using appropriate computational tools and methodologies.

• Assist in the preparation and management of project budgets and expenditure tracking.

• Contribute to the preparation of technical reports, research deliverables, and peer-reviewed journal manuscripts.

• Work closely with project team members to ensure timely and high-quality research outputs.

Job Requirements

• Master's degree in a STEM-related discipline (e.g. engineering, science, computer science, or related fields).

• Ability to work effectively in a team-based research environment, including participation in fieldwork.

• Strong attention to detail and a meticulous approach to data collection, recording, and management.

• Working knowledge of MATLAB and Python for data processing and analysis.

• Experience with 3D modelling software and developing 3D models from photographs using photogrammetry techniques.

• Good written and oral communication skills, with the ability to contribute to technical reports and academic publications.

• Possession of a valid Class 3 driving licence and willingness to travel for field tests as required.

We regret to inform that only shortlisted candidates will be notified.