Research Engineer, Materials Science & Chemistry, IMRE

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

A*STAR is seeking an AI-Driven Polymer Design Engineer to design and optimise functional polymer systems using AI-enabled approaches with a focus on stimuli-responsive and hydrogel formulations for biomedical and delivery applications. The role spans polymer chemistry, physical chemistry, and machine learning, contributing to self-driving lab and high-throughput materials discovery capabilities.

You will collaborate across teams and with external partners to advance materials design and

Qualifications

• Hands-on experience in polymer/materials synthesis, formulation and characterisation.

Responsibilities

• Synthesize, formulate, and characterise functional polymers with tunable self-assembly, gelation, and mechanical properties.
• Develop predictive (classification and regression) models relating synthesis, processing, formulation, and structure to material behaviour and performance.
• Engineer descriptors and features to improve model interpretability and predictive accuracy.
• Establish structure–property relationships using materials characterisation to guide formulation design.
• Build data-driven design tools that translate empirical formulation knowledge into material selection and optimisation for various industrial applications.
• Contribute to lab automation, data infrastructure (SQL-based), and integrated experimental–ML workflows for accelerated discovery.
• Prepare technical reports, publications, and disclosures; collaborate across teams and with external partners.

Skills

Python

SQL

ML model development

Data analytics

Education

Bachelor's or Master's in Materials Science and Engineering, Polymer Chemistry, Chemical Engineering, Chemistry

Tools

Python

SQL

Job description

AI-Driven Polymer Design Engineer

We are seeking a motivated Research Engineer to design and optimise functional polymer systems using AI-enabled approaches, with an initial focus on stimuli-responsive and hydrogel formulations for biomedical and delivery applications, expanding into polymers for other key industry applications. The role bridges polymer chemistry, physical chemistry, and machine learning, contributing to the development of self-driving lab and high-throughput materials discovery capabilities.

Job Responsibilities

• Synthesise, formulate, and characterise functional polymers with tunable self-assembly, gelation, and mechanical properties.
• Develop predictive (classification and regression) models that relate synthesis, processing conditions, formulation, and structure to material behaviour and performance.
• Engineer descriptors and features that improve model interpretability and predictive accuracy.
• Establish structure–property relationships using materials characterisation to guide formulation design.
• Build data-driven design tools that translate empirical formulation knowledge into material selection and optimisation for various industrial applications.
• Contribute to lab automation, data infrastructure (SQL-based), and integrated experimental–ML workflows for accelerated discovery.
• Prepare technical reports, publications, and disclosures; collaborate across teams and with external partners.

Requirements

• Bachelor's or Master's degree in Materials Science and Engineering, Polymer Chemistry, Chemical Engineering, Chemistry, or a related field.
• Hands‑on experience in polymer/materials synthesis, formulation, and characterisation.
• Programming and data‑analytics capability (e.g. Python, SQL), including ML model development and validation.
• Ability to work independently and within a multidisciplinary team, with strong communication and documentation skills.

Strong Plus If Candidate Have

• Experience integrating experimental data with machine learning for materials design.
• Familiarity with polymers, polymers used in advanced packaging and advanced manufacturing, hydrogels, and drug‑delivery systems.
• Exposure to lab automation, high‑throughput experimentation, or self‑driving lab concepts.
• Track record of publications and/or contributions to intellectual property.