Full Time Postdoctoral Fellow (AI-ML-driven High-throughput Material Synthesis and Characterization) Jobs, in Nanyang Technological University Southwest (Singapore) - Maukerja

The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological Materials, Bioinspired Materials and Sustainable Materials.

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We are looking for a Postdoctoral Fellow to conduct research in the project “AxCIS Lab – AI xploration of Catalysis with Inorganics and Surfaces,” a collaborative research programme involving Nanyang Technological University (NTU), National University of Singapore (NUS), and BEARS. The role will focus on developing and implementing AI/ML-driven high-throughput, closed-loop experimentation for the discovery and optimization of electrocatalytic materials for CO₂ reduction. The successful candidate will design and integrate automated materials fabrication and characterization workflows with agentic AI systems to enable autonomous experimentation and data-driven decision-making. Key research activities will include multi-parameter optimization of experimental process spaces, investigation of catalyst structure–performance relationships, and development of strategies for in-situ catalyst regeneration. The candidate will also collaborate closely with computational researchers and electrochemical characterization researchers to validate predictive models, accelerate catalyst discovery, and contribute to the overall objectives of the AxCIS research programme.

Key Responsibilities:

• Conduct AI/ML-driven high-throughput, closed-loop experimentation to accelerate the discovery, evaluation, and optimization of electrocatalytic materials for CO2 reduction.
• Design and develop automated workflows for materials fabrication and characterization, integrating AI agents to enable autonomous experimentation and decision-making.
• Develop and implement multi-parameter optimization strategies to explore complex experimental process spaces, including the optimization of in-situ catalyst regeneration processes.
• Collaborate closely with computational researchers to evaluate and validate predictive models, integrating experimental feedback to improve model performance.
• Analyze and interpret experimental results, investigate structure–performance relationships, and identify key factors influencing electrocatalytic performance.
• Prepare and publish high-quality research articles in peer-reviewed scientific journals.
Communicate research findings through presentations at scientific conferences, project meetings, and programme reviews.

Job Requirements:

• A PhD degree in Chemistry, Materials Science and Engineering, Chemical Engineering, or a closely related discipline.
• Strong hands-on experience in designing and conducting AI/ML-driven high-throughput experiments, preferably in materials science, electrochemistry, or related research fields.
• Experience in developing and implementing AI-agentic workflows for experimental automation, data analysis, and autonomous decision-making.
• Demonstrated experience in multi-parameter experimental optimization, structure–performance analysis, and/or mechanistic investigations.
• Familiarity with machine learning techniques, optimization algorithms, and data-driven approaches for materials discovery and process optimization.
• A strong research track record demonstrated by publications in peer-reviewed scientific journals, with the ability to independently plan, execute, and manage research activities.
• Strong analytical and problem-solving skills, with the ability to systematically investigate complex scientific and technical challenges.
Excellent teamwork, mentoring, and organizational skills, along with strong written and verbal communication abilities.

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