Research Fellow / Research Engineer (AI for Civil Engineering) - MM1

Singapore Institute of Technology; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading university in Singapore is seeking a Research Fellow/Research Engineer focused on AI for Civil Engineering. The candidate will manage research projects, enhance QA/QC processes, and develop AI models to automate tasks. Qualified individuals should hold a Master’s degree in a relevant field and possess a strong foundation in data engineering and AI/ML development. Excellent communication and analytical skills are essential for this role.

Qualifications

• Master’s degree in a relevant field required.
• Strong foundation in data engineering and AI/ML development.
• Experience with real-world datasets is necessary.

Responsibilities

• Manage the research project with the Principal Investigator and team.
• Conduct evaluations to enhance QA/QC efficiency.
• Develop and validate AI models for automation.

Skills

Data engineering

AI/ML development

Software systems

Data analytics

Python

Interpersonal communication

Education

Master’s degree in Computer Science, Data Science, Civil/Construction Engineering or equivalent

Tools

Finite element modelling

Statistical software

Job description

Job Description

Research Fellow / Research Engineer (AI for Civil Engineering) - MM1

Job Description

Research Fellow / Research Engineer (AI for Civil Engineering) - MM1

Posting Start Date: 17/03/2026

Schemes of Service: Research

Division: Engineering

Employment Type: Fixed Term

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

Key Responsibilities

• Participate in and manage the research project with the Principal Investigator (PI), co-PIs, research team and industry partners to ensure all project deliverables are met.
• Conduct insights-driven evaluations of existing QA/QC testing practices and identify opportunities to enhance efficiency and reduce redundant testing while maintaining compliance.
• Curate, standardize and integrate historical and statistical data to form the foundation of a structured database for construction QA/QC and traceability.
• Design and implement a digital chain-of-custody framework that captures assurance records including mill certificates, fabrication data, inspection results and carbon footprint information.
• Develop, train and validate AI models to automate QA/QC processes, enabling real-time verification, anomaly detection and consistency checks across records.
• Develop a cloud-based verification platform for digital data collection and real-time analytics.
• Develop and apply non-destructive testing (NDT) methods and data-driven approaches to determine material properties of reclaimed steel.
• Investigate geometric imperfections, accumulated deformations, and their impact on structural performance, particularly for compression members.
• Develop data-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems.
• Prepare and draft technical reports, conference/journal papers, and presentation materials; present research findings to internal reviews, industry partners and stakeholders.
• Engage with industry stakeholders throughout development, testing and refinement to ensure alignment with real-world requirements.
• To communicate in any relevant internal or external stakeholders to ensure project deliverables are met.
• Any ad-hoc duties assigned by Supervisor.
  
  

Job Requirements

• Master’s degree in Computer Science, Data Science, Civil/Construction Engineering, Engineering Systems or equivalent.
• Strong foundation in data engineering, AI/ML development, and software systems (e.g., data pipelines, cloud services, dashboards, web-based application development).
• Experience in handling real-world datasets and model development/deployment workflows.
• Domain expertise in building construction/civil/structural engineering, including familiarity with building management systems, software integration, data collection and analysis.
• Knowledge of construction QA/QC and quality control workflows (e.g., inspection processes, test documentation, traceability and compliance records) is an advantage.
• Strong background in structural engineering, structural mechanics, or steel structures.
• Familiarity with non-destructive testing (NDT) techniques for structural materials is desirable.
• Experience with structural modelling and analysis tools (e.g., finite element modelling, structural simulation) is desirable.
• Good interpersonal, written and verbal communication skills, with the ability to work independently and in multidisciplinary teams.
• Strong analytical and conceptual abilities; able to work both in a team and independently.
• Able to work under pressure and meet deadlines.
  
  

Key Competencies

• Proficient in Python or other equivalent programming languages.
• Strong foundation in data analytics and data-driven modelling.
• Able to build and maintain strong working relationships with people within and external to the university.
• Able to work independently with strong data analytical skills, communication and interpersonal skills.
• Self-directed learner who believes in continuous learning and development.
• Proficient in technical writing and presentation.
• Possess strong analytical and critical thinking skills.
• Show strong initiative and take ownership of work.