- Singapore Singapore
全职 Research Engineer (PREPARE) - EA9 工作, 薪水, Singapore Institute Of Technology 公司招聘中 - Ricebowl
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Job Description ************* ************* .unify-apply-now: focus, ************* ************* ************* ************* .unify-apply-now: focus, ************* ************* .unify-apply-Research Engineer (PREPARE) - EA9
Posting Start Date: 28/08/2026
Schemes of Service: Research
Division: Engineering
Employment Type: Fixed Term
Job Purpose
As a University of Applied Learning, SIT works closely with industry and research partners in our research pursuits. Our research staff will have the opportunity to develop industry-relevant applied research skillsets while working on multidisciplinary research projects.
The primary responsibility of this role is to support SIT's research activities under the Programme for Research in Epidemic Preparedness and REsponse (PREPARE) , focusing on experimental studies and data-driven analysis of droplet and aerosol generation, characterisation and dispersion. The Research Engineer will contribute to experimental data collection, processing and quality enhancement, feature extraction and pattern analysis, with the aim of identifying meaningful relationships between measured droplet/aerosol characteristics, experimental conditions and other relevant markers. The role will also support the generation of robust datasets for subsequent modelling and validation activities.
Key Responsibilities
Participate in and manage the research project together with the Principal Investigator (PI), Co-PI, and research team members to ensure project deliverables are achieved.
Undertake the following responsibilities in the project:
i. Experimental Studies and Data Collection
Set up and conduct experiments to characterise droplet/aerosol generation and dispersion under different operating and environmental conditions.
Operate relevant measurement, imaging and data acquisition equipment and ensure reliable experimental data collection.
ii. Data Processing and Feature Engineering
Clean, process and analyse experimental, imaging and sensor datasets using Python, MATLAB and/or other appropriate tools.
Extract and develop robust features from raw measurements to improve data quality and support downstream analysis.
iii. Pattern and Correlation Analysis
Apply statistical and data-driven methods to identify trends, patterns and correlations within experimental datasets.
Investigate relationships between extracted features and experimental conditions, physiological parameters or other relevant markers where available.
iv. Research Analysis and Validation
Analyse and visualise experimental results and prepare datasets for modelling and validation by the wider research team.
Support interpretation of results and comparison with computational or numerical models where applicable.
v. Research and Technical Support
Conduct literature reviews and prepare technical reports, publications and presentation materials.
Carry out Risk Assessment and ensure compliance with Workplace Safety and Health regulations.
Coordinate procurement and liaison with vendors/suppliers where required.
Assist in co-supervision of Final Year Project (FYP) or capstone students together with the project PI.
The Research Staff is to communicate and liase with internal and external stakeholders to ensure project deliverables are met. To support any additiona ad-hoc duties assigned by the Supervisor.
Job Requirements
Bachelor's or Master's degree in Engineering, Data Science, Computer Science, or a related discipline.
Proficiency in Python and/or MATLAB for data processing, analysis and visualisation.
Experience with data analysis, signal/image processing, statistical analysis and/or machine learning.
Strong analytical and problem-solving skills.
Good written and verbal communication skills and ability to work independently and within a multidisciplinary team.
The following will be advantageous:
Experience with experimental or sensor datasets.
Experience with feature engineering, pattern recognition or machine learning.
Knowledge of fluid mechanics, aerosol/droplet behaviour, or experimental measurement techniques
Key Competencies
Strong analytical and data-driven problem-solving skills.
Ability to translate raw experimental data into reliable and interpretable quantitative features.
Ability to critically assess data quality and distinguish meaningful patterns from experimental variability and noise.
Self-directed learner with strong initiative and ownership of work.
Able to build and maintain strong working relationships with internal and external stakeholders.
Proficient in technical writing, data visualisation and presentation.
Able to work effectively across both experimental and computational aspects of multidisciplinary research
Major Challenges
Processing heterogeneous and potentially noisy experimental datasets obtained from multiple measurement techniques.
Developing robust features that remain consistent across different experimental runs and operating conditions.
Distinguishing meaningful physical patterns and correlations from measurement noise and experimental variability.
Integrating imaging, particle measurement and other experimental data into coherent datasets for analysis.
Identifying meaningful relationships between droplet/aerosol characteristics and other available experimental or physiological parameters
Producing high-quality datasets and features suitable for subsequent modelling, validation and research interpretation.
************* ************* .unify-apply-now: focus, ************* ************* ************* ************* .unify-apply-now: focus, ************* ************* .unify-apply-
Posting Start Date: 28/08/2026
Schemes of Service: Research
Division: Engineering
Employment Type: Fixed Term
Job Purpose
As a University of Applied Learning, SIT works closely with industry and research partners in our research pursuits. Our research staff will have the opportunity to develop industry-relevant applied research skillsets while working on multidisciplinary research projects.
The primary responsibility of this role is to support SIT's research activities under the Programme for Research in Epidemic Preparedness and REsponse (PREPARE) , focusing on experimental studies and data-driven analysis of droplet and aerosol generation, characterisation and dispersion. The Research Engineer will contribute to experimental data collection, processing and quality enhancement, feature extraction and pattern analysis, with the aim of identifying meaningful relationships between measured droplet/aerosol characteristics, experimental conditions and other relevant markers. The role will also support the generation of robust datasets for subsequent modelling and validation activities.
Key Responsibilities
Participate in and manage the research project together with the Principal Investigator (PI), Co-PI, and research team members to ensure project deliverables are achieved.
Undertake the following responsibilities in the project:
i. Experimental Studies and Data Collection
Set up and conduct experiments to characterise droplet/aerosol generation and dispersion under different operating and environmental conditions.
Operate relevant measurement, imaging and data acquisition equipment and ensure reliable experimental data collection.
ii. Data Processing and Feature Engineering
Clean, process and analyse experimental, imaging and sensor datasets using Python, MATLAB and/or other appropriate tools.
Extract and develop robust features from raw measurements to improve data quality and support downstream analysis.
iii. Pattern and Correlation Analysis
Apply statistical and data-driven methods to identify trends, patterns and correlations within experimental datasets.
Investigate relationships between extracted features and experimental conditions, physiological parameters or other relevant markers where available.
iv. Research Analysis and Validation
Analyse and visualise experimental results and prepare datasets for modelling and validation by the wider research team.
Support interpretation of results and comparison with computational or numerical models where applicable.
v. Research and Technical Support
Conduct literature reviews and prepare technical reports, publications and presentation materials.
Carry out Risk Assessment and ensure compliance with Workplace Safety and Health regulations.
Coordinate procurement and liaison with vendors/suppliers where required.
Assist in co-supervision of Final Year Project (FYP) or capstone students together with the project PI.
The Research Staff is to communicate and liase with internal and external stakeholders to ensure project deliverables are met. To support any additiona ad-hoc duties assigned by the Supervisor.
Job Requirements
Bachelor's or Master's degree in Engineering, Data Science, Computer Science, or a related discipline.
Proficiency in Python and/or MATLAB for data processing, analysis and visualisation.
Experience with data analysis, signal/image processing, statistical analysis and/or machine learning.
Strong analytical and problem-solving skills.
Good written and verbal communication skills and ability to work independently and within a multidisciplinary team.
The following will be advantageous:
Experience with experimental or sensor datasets.
Experience with feature engineering, pattern recognition or machine learning.
Knowledge of fluid mechanics, aerosol/droplet behaviour, or experimental measurement techniques
Key Competencies
Strong analytical and data-driven problem-solving skills.
Ability to translate raw experimental data into reliable and interpretable quantitative features.
Ability to critically assess data quality and distinguish meaningful patterns from experimental variability and noise.
Self-directed learner with strong initiative and ownership of work.
Able to build and maintain strong working relationships with internal and external stakeholders.
Proficient in technical writing, data visualisation and presentation.
Able to work effectively across both experimental and computational aspects of multidisciplinary research
Major Challenges
Processing heterogeneous and potentially noisy experimental datasets obtained from multiple measurement techniques.
Developing robust features that remain consistent across different experimental runs and operating conditions.
Distinguishing meaningful physical patterns and correlations from measurement noise and experimental variability.
Integrating imaging, particle measurement and other experimental data into coherent datasets for analysis.
Identifying meaningful relationships between droplet/aerosol characteristics and other available experimental or physiological parameters
Producing high-quality datasets and features suitable for subsequent modelling, validation and research interpretation.
************* ************* .unify-apply-now: focus, ************* ************* ************* ************* .unify-apply-now: focus, ************* ************* .unify-apply-
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