AI-Driven Postdoc in Catalysis & Autonomous Discovery

ntu; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

NTU's School of Materials Science and Engineering seeks a Postdoctoral Fellow to advance AI/ML-driven high-throughput, closed-loop experiments for electrocatalytic CO2 reduction. The role integrates automated fabrication/characterization workflows with agentic AI systems to enable autonomous experimentation and data-driven decisions.

Collaboration with computational and electrochemical teams is essential. Key tasks include multi-parameter optimization, evaluating catalyst structure–performance

Qualifications

• PhD in a closely related discipline with strong research output.
• Hands-on experience in AI/ML-driven high-throughput experiments.
• Experience with AI-agentic workflows for automation and data analysis.

Responsibilities

• Conduct AI/ML-driven high-throughput, closed-loop experiments for CO2 reduction catalysts.
• Design automated workflows for materials fabrication and characterization with AI agents.
• Develop multi-parameter optimization strategies within experimental spaces.
• Collaborate with computational researchers to validate predictive models.
• Analyze structure–performance relationships and identify key factors.
• Publish in peer-reviewed journals and present findings at conferences.

Skills

AI/ML-driven high-throughput

multi-parameter optimization

autonomous experimentation

data-driven decision making

catalysis materials

Education

PhD in Chemistry

PhD in Materials Science and Engineering

PhD in Chemical Engineering

Tools

Python

TensorFlow

PyTorch

Job description

NTU's School of Materials Science and Engineering seeks a Postdoctoral Fellow to advance AI/ML-driven high-throughput, closed-loop experiments for electrocatalytic CO2 reduction. The role integrates automated fabrication/characterization workflows with agentic AI systems to enable autonomous experimentation and data-driven decisions.

Collaboration with computational and electrochemical teams is essential. Key tasks include multi-parameter optimization, evaluating catalyst structure–performance