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.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Engineer-%28Mechatronics-Engineering%29/32313-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

We are looking for a highly motivated and passionate research engineer to work on a funded research project that investigates the development of personalized cooling systems leveraging thermoelectric cooling technology. The aim of the research is to design a personalized cooling device for mixed-mode ventilation and natural ventilation environments in the tropics.  The successful applicant will be supervised by the principal investigator Dr. Adrian Chong from the Department of the Built Environment, College of Design and Engineering, National University of Singapore.

•    Work closely with team members from thermal, fluid  and material engineering background to setup sensors and prototype the device.
•    Setup PID for fans and pumps to respond for changes and optimize electric power consumption for increasing the efficiency of the device on a component level and the overall system.
•    Conduct parametric experiments to evaluate system behavior under varying operational conditions, including power supply constraints, ambient temperatures, and human-machine interface factors.
•    Develop PCB for the device.
•    Document project progress in technical reports, presentations, and peer-reviewed publications.
•    Collaborate with cross-functional teams, including mechanical, materials, and Fluid mechanics engineers, and potentially external industry partners.
•    Organize, analyze and publish research in peer-reviewed scientific journals.
•    Perform other duties as assigned.

Job Requirements

Qualifications and Skills:

•    Bachelor’s or Master’ Degree in Electrical Engineering, Mechatronics, Control Systems, or related field
•    Strong experience with LabVIEW software (or any open source alternative)
•    Knowledge of data acquisition systems (e.g., NI DAQ hardware)
•    Understanding of control systems theory (PID, system dynamics, feedback control)
•    Experience with sensor integration and instrumentation (Velocity, temperature, pressure, electric power measurements)
•    Ability to analyze, interpret and present experimental data
Strong problem-solving and troubleshooting skills

Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty.

A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing.

Key Responsibilities:

• Conduct programming and software development for graph data management.

• Design and implement machine learning models for optimizing graph data management.

• Conduct experiments and evaluations of the designed algorithms and systems.

• Help with research presentation works such as high-quality paper writing.

Job Requirements:

• Preferably Master in computer engineering, Computer Science, Electronics Engineering or equivalent.

• Independent, highly analytical, proactive and a team player

• Excellent teamwork and verbal, written communication skills

• In-depth knowledge relevant to the project domain.

• Demonstrated capability to conduct innovative research.

• The ability and willingness to adapt and incorporate novel ideas and approaches in scientific industry.

• Demonstrated ability to formulate hypothesis and design effective experimental plans.

• Ability to design and implement new experimental methods.

• High-level expertise in the required experiment or modelling methods.

• Ability to initiate collaboration research in multidisciplinary team.

• Oversee and report project progress.

• Ability and willingness to work some flexible hours.

We regret that only shortlisted candidates will be notified.

Established in 2010, the Energy Research Institute @ NTU (ERI@N) is a pan-university research institute that focuses on systems-level research for tropical megacities. It performs translational research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology deployment enabling validation and demonstration of real-world applications.

For more details, please view https://www.ntu.edu.sg/erian

We are looking for a Research Assistant to support an AI-enabled battery recycling research project that advances sustainable energy and circular economy goals. The role will focus on developing data-driven models and decision tools to improve the efficiency, yield, and cost effectiveness of battery recycling workflows, supporting experimental and simulation studies across the battery lifecycle, and implementing algorithms for process monitoring and optimization so that recycling operations can be more reliable, scalable, and environmentally responsible.

Key Responsibilities:

• Conduct literature review and problem scoping on battery recycling, including process routes such as hydrometallurgy, pyrometallurgy, and direct recycling.

• Build datasets from experiments, sensors, and public sources, and then perform data cleaning, labeling, and feature engineering for battery recycling tasks.

• Develop AI and machine learning models for recycling process prediction and decision support, such as forecasting metal recovery, impurity levels, energy use, and emissions.

• Develop optimization and control methods for recycling resource management, such as reagent usage, temperature profiles, scheduling, and throughput, under safety and quality constraints.

• Support experimental coordination and analysis, including interpretation of characterization and assay results (as applicable), and then translate findings into model inputs and evaluation.

• Prepare technical reports, documentation, and research manuscripts, and also contribute to project presentations and meeting updates.

• Collaborate with the PI and team members, and coordinate with external partners when needed, to ensure milestones and deliverables are met.

Job Requirements:

• Bachelor qualification degree in Computer Science, Electrical Engineering, or related disciplines

• Strong research experience with a good track record of publications.

• Familiarity with machine learning and optimization, such as supervised learning, reinforcement learning, or constrained optimization.

• Good written and oral communication skills for preparing technical reports, presentations, and draft manuscripts.

• Proficiency in hard skills, such as implementing and evaluating algorithms in Python, and maintaining reproducible experimentation.

• Competent in soft skills, such as problem scoping and analytical skills for experimental results interpretation.

• Interpersonal skill, such as good teamwork and collaboration skills to work closely with the PI, team members, and external partners.

• Entry level candidates are welcome to apply.

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

The School of Chemistry, Chemical Engineering and Biotechnology (CCEB) at NTU Singapore, in collaboration with Wageningen University & Research (WUR), is part of Singapore Indoor Farming System (SingFarm)—a CREATE-NRF programme focused on building a sustainable, data-driven food production model with strong industry relevance to contribute Singapore’s ambition in achieving 20 per cent local production of its fiber (vegetables, mushrooms etc.) by 2035. The candidate would be working in a multinational team at the forefront of Singapore’s agricultural innovation and food security.

The successful candidate will work in Work Package 3.3: Urban Demo Lab. In this role, you help translate research results from across the program into demonstrators and settings for collaborative exploration that allow key stakeholders to engage with emerging food production technologies – and each other. The Urban Demo Lab serves as a platform where researchers, industry partners, and other stakeholders explore applications, provide critical design feedback and identify opportunities for further innovation: it serves as an attractor for future projects and strategic alliances that maximize the urban impact of ongoing research and development.

As an innovation facilitator, your work provides the active interface that is needed between research, technology translation, and stakeholder engagement. By stimulating dialogue and collaborative learning, you identify critical friction zones and promising ways forward, effectively accelerating the uptake of scientific innovations in real-world applications.

Key Responsibilities:

• Collaborate with research teams to identify key elements of research suitable for demonstration.

• Facilitate workshops, meetings, and stakeholder sessions to explore research outcomes, gather feedback, and identify innovation opportunities.

• Translate scientific insights into demonstrators, prototypes, or other formats such as an overall performance dashboard that communicate application potential to diverse audiences.

• Contribute to identifying and engaging potential partners or funders interested in further development of promising ideas.

Job Requirements:

• Completed PhD in a relevant field (e.g., food systems, food production, sustainability transitions or related fields).

• Systems thinker with an interest in innovation in food production and controlled environment agriculture

• Experience translating scientific research into practical applications, demonstrators or stakeholder-facing outputs.

• Excellent communication skills and ability to work across disciplinary and organizational boundaries.

• Experience facilitating interdisciplinary collaboration, workshops, or stakeholder engagement is an advantage.

We seek a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.

We regret that only shortlisted candidates will be notified.

The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

For more details, please view https://www.ntu.edu.sg/mae/research.

The research fellow will focus on foundation model-based world model development for embodied intelligence, and robotics.

Key Responsibilities:

• The research fellow will be leading the development of foundation model-based world model development for embodied intelligence and robotics.

• The work will involve model development, model training, test rig set-up, and experimental validation.

Job Requirements:

• The candidate must at least have a PhD Degree in AI, Robotics, Automation, Mechanical Engineering, Control Engineering, Mechatronics, Computer Science, etc.

• Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation.

• Candidates having relevant research or working experience in computer vision, VLM/VLA and end-to-end autonomous driving will be preferred.

• Good communications skills and the ability to work independently and facilitate projects with minimal supervision.

• Able to contribute to Journal/Conference papers with good presentation skills.

• Able to plan and organize well and adhere to deadlines

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

The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE prides itself in its excellent research capabilities in areas including advanced manufacturing, aerospace, biomedical, energy, industrial engineering, maritime engineering, robotics, etc. The school is equipped with state-of-the-art research infrastructure, housing a comprehensive range of cluster laboratories, test bedding facilities, research centres/institutes and corporate laboratories. Cutting-edge research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in developing new competencies to support the growth and competitiveness of our engineering sector in the global landscape. MAE has grown to be leader in Engineering Research, ranking amongst the top engineering schools in the world.

For more details, please view https://www.ntu.edu.sg/mae/research.

The research associate will focus on Vision-Language Model based situation awareness and decision-making for autonomous vehicles

Key Responsibilities:

• The research associate will be leading the development of Vision-Language Model based situation awareness and decision-making models for autonomous vehicles.

• The work will involve model training, test rig set-up, and experimental validation

Job Requirements:

• The candidate must at least have a Master Degree in AI, Robotics, Automation, Mechanical Engineering, Control Engineering, Mechatronics, Computer Science, etc.

• Strong background in AI, Large Language Model, autonomous driving, deep learning, interaction modelling, prediction, robotics and automation.

• Candidates having relevant research or working experience in LLM/VLM and AI decision-making algorithms for end-to-end autonomous driving will be preferred.

• Good communications skills and the ability to work independently and facilitate projects with minimal supervision.

• Able to contribute to Journal/Conference papers with good presentation skills.

• Able to plan and organize well and adhere to deadlines

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

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Visiting-Research-Fellow-%28National-Centre-for-Engineering-Biology%29/32344-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

We are seeking a talented Visiting Research Fellow to join the National Centre for Engineering Biology, Singapore (NCEB), at the National University of Singapore. NCEB aims to advance engineering biology by developing microbial cells that enable biomanufacturing.

The Visiting Research Fellow (RF) will work closely with the Principal Investigator and research team to develop robust microbial cell platforms for biomanufacturing. The RF will design and implement strain engineering strategies, optimise metabolic pathways and cultivation conditions, and generate high-quality experimental data.

Key Responsibilities: 
1. Design, build, and characterise engineered microbial strains for production of target molecules (e.g., enzymes, chemicals, or biomaterials).
2. Develop and optimise genetic constructs and genome engineering workflows (e.g., CRISPR, recombineering, plasmid design, promoter/RBS tuning).
3. Perform metabolic pathway design and optimisation using rational design and/or high-throughput approaches; analyse data to identify bottlenecks and improvement opportunities.
4. Develop and apply analytical assays (e.g., HPLC/GC, LC-MS, plate-based assays) to quantify key metabolites.
5. Document and communicate results through lab records, reports, and presentations; contribute to manuscripts, protocols, and (where applicable) invention disclosures.

Remuneration will be commensurate with the candidate's qualifications and experience. Informal enquiries are welcome and should be made to: Ms Suhana Sulaiman, at suhana_s@nus.edu.sg 

Formal application: Please submit your application, indicating job reference number and current/expected salary, supported by a detailed CV (including personal particulars, academic and employment history, complete list of publications/oral presentations and full contacts of 3 referees). Only shortlisted candidates will be notified.

Qualifications

The ideal candidate will:
1. Hold a PhD in Synthetic Biology, Microbiology, Biotechnology, Bioengineering, Chemical Engineering, or a related field.
2. Demonstrated experience in microbial strain engineering and molecular biology (e.g., cloning, genome editing, strain construction and verification).
3. Strong experimental design and data analysis skills, with the ability to interpret results and troubleshoot across the build-test-learn cycle.

Highly desirable:
1. Hands-on experience with fermentation/bioreactors and bioprocess development (media optimisation, feeding strategies, scale-down models).
2. Experience with omics/automation tools (e.g., RNA-seq, proteomics, metabolomics, high-throughput screening).

Established in 2010, the Energy Research Institute @ NTU (ERI@N) is a pan-university research institute that focuses on systems-level research for tropical megacities. It performs translational research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which focus on translational Research, Development & Deployment which focus on specific area of the energy value chain, and a number of Living labs and Testbeds which facilitate large scale technology deployment enabling validation and demonstration of real-world applications.

For more details, please view https://www.ntu.edu.sg/erian

You will be part of a dynamic research team working on topics relevant to the Sustainable Built Environment (SBE). The SBE team focuses on building energy modelling and simulation, decarbonization strategies for the built environment and R&D of green building technologies such as HVAC systems, thermal energy storage, data-driven building control systems and other novel solutions for the urban built environment sector. This position is to recruit a member of the research team to work on a project on sustainable hydronic cooling system

Key Responsibilities:

• You will need to actively support SBE projects at ERI@N in terms of project R&D, technology evaluation, project coordination, and stakeholder interfacing.

• The role will include the following:

  • Assist in coordination with project partners, industry and other external stakeholders.

  • Lead technical deployment of testbeds and instrumentation for testing and validation of hydronic cooling/green building solutions e.g. equipment or hardware/software systems.

  • Conduct research, analysis and performance assessment of project testbeds.

  • Lead in preparation and submission of project reports/test reports.

  • Work closely with industry partners to realize synergies in testbeds, site implementation and demonstration.

  • Engage stakeholders at management and working levels internally as well as with external funding agencies, collaborators and industry partners, including delivering presentations and guiding discussions or workshops as necessary.

  • Assist in authoring of technical publications for completed or ongoing research projects.

• You will be an integral part of the SBE team and will need to participate in project discussions especially related to project implementation details.

• Familiarity with district-level cooling systems, green building technology, HVAC systems, sustainable building design, and energy measurement and verification, for built environment applications is preferred.

• Knowledge in test instrumentation, PLC programming and cloud-based/IoT applications is preferred

Job Requirements:

• Masters degree in engineering (e.g. Mechanical, Clean/Renewable Energy or Building related) or sustainable buildings design.

• Experience in sustainable design/analysis for the built environment, Green Mark or LEED projects, M&E construction and development (min 2yrs preferred).

• Proficient in MS Office applications, PLC programming, IoT/cloud development, Modelica/Dymola or energy modelling software.

• Should be a very hands-on person, willing to take on non-routine tasks.

• Possess good communication (written and verbal) skills.

• Able to manage multiple stakeholders and work under tight timelines.

• Positive attitude, ability to work independently as well as in a team

• Prior experience in sustainability standards and green building accreditations would be advantageous.

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