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About the role
This role sits with the SOM group's Energy and Resource Analytics team in Singapore, where you will build and integrate AI/ML surrogate modelling solutions for industrial water use and wastewater treatment. The work supports sustainability goals such as better water recycling and lower operational carbon intensity, and it suits someone with a process engineering background who is comfortable with data-driven methods. You will also assess emerging technologies and design end-to-end workflows that connect data to high-fidelity simulations with scalable AI surrogate models for validation, deployment and implementation at industrial sites.
Senior Research Engineer (Group: SOM) [1] Energy and Resource Analytics, Data Analytics… job at A*STAR, Singapore | WorkJio
Lead and support capability development in process simulation and surrogate modelling in line with the technical roadmap for industrial water and wastewater sustainability work
Work across teams to bring simulation-driven insights and surrogate models into a wider digital twin for exploring optimisation strategies
Evaluate and integrate current research in physics-based modelling and AI-driven emulation into practical, industry-ready workflows
Design and train machine learning models that emulate or approximate complex physical simulations for faster predictive analysis
Apply data-driven techniques to draw out key patterns and relationships from simulation and experimental datasets
Develop interpretable and robust surrogate models for process design, optimisation and control applications
Design and implement end-to-end workflows covering process simulation, data extraction, feature engineering, ML model development, validation and deployment
Work with IT and data engineering teams to connect models to existing data infrastructure for maintainable MLOps workflows and lifecycle management
Collect, preprocess and analyse datasets from simulations, experiments and industrial systems
Evaluate the sensitivity and performance of process parameters through simulation and surrogate-based studies
Work closely with domain experts to validate correlations and surrogate model predictions and to quantify model uncertainty
Communicate findings, methodologies and recommendations to technical and non-technical stakeholders
Collaborate with process engineers, plant operators and R&D teams to keep work aligned with process improvement objectives
Prepare reports, technical documentation, simulation validation summaries and deployment guides
Contribute to secondary roles for the team, Group, Organisation and A*STAR
Good to know
Based in Singapore
Requirements
Bachelors or Masters in Mechanical, Chemical or Industrial Engineering, Data Science, or a related field with a focus on process modelling or data analytics
At least 2 years of experience in process simulation or applying AI/ML to engineering problems
Experience with water quality related data or processes is preferred
Proven experience developing and deploying surrogate models or applying data analytics insights to data-driven decision making tools
Strong programming and data science skills in Python, MATLAB, TensorFlow, PyTorch and Scikit-learn
Familiarity with surrogate modelling techniques and ML/AI workflows
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