Postdoctoral Fellow (AI/ML-driven High-throughput Material Synthesis and Characterization)

Nanyang Technological University; Singapore; On-site; SGD 72,000 - 96,000; Full time

Job summary

Nanyang Technological University invites applications for a Postdoctoral Fellow to advance AI/ML‑driven high‑throughput, closed‑loop experiments for discovering and optimising electrocatalytic materials for CO₂ reduction. The role integrates automated fabrication and characterisation workflows with agentic AI systems to drive autonomous experimentation and data‑driven decisions.

The successful candidate will collaborate with computational and electrochemical researchers to validate models,

Qualifications

• PhD in Chemistry, Materials Science and Engineering, Chemical Engineering, or closely related field.
• Strong hands-on experience designing AI/ML-driven high-throughput experiments.
• Experience developing AI-agentic workflows for automated data collection and decision making.
• Proven ability to analyze structure–performance relationships in electrocatalytic materials.
• Strong publication record in peer‑reviewed journals.

Responsibilities

• Conduct AI/ML-driven high-throughput, closed-loop experimentation to accelerate discovery of electrocatalytic materials for CO2 reduction.
• Design automated workflows for materials fabrication and characterisation, integrating AI agents for autonomous experimentation.
• Develop multi-parameter optimisation strategies to explore complex process spaces including in-situ catalyst regeneration.
• Collaborate with computational researchers to evaluate and validate predictive models with experimental feedback.
• Analyse and interpret experimental results to identify factors influencing performance.
• Publish high-quality research articles in peer-reviewed journals and present at conferences.

Skills

AI/ML workflows

High-throughput experiments

Autonomous experimentation

Materials science

Electrochemistry

Data analysis

Model validation

Education

PhD in Chemistry

PhD in Materials Science and Engineering

PhD in Chemical Engineering

Job description

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. For more details, please view https://www.ntu.edu.sg/mse/research.

Position

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 characterisation workflows with agentic AI systems to enable autonomous experimentation and data-driven decision-making. Key research activities will include multi-parameter optimisation 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 characterisation 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 optimisation of electrocatalytic materials for CO2 reduction.
• Design and develop automated workflows for materials fabrication and characterisation, integrating AI agents to enable autonomous experimentation and decision‑making.
• Develop and implement multi‑parameter optimisation strategies to explore complex experimental process spaces, including the optimisation of in‑situ catalyst regeneration processes.
• Collaborate closely with computational researchers to evaluate and validate predictive models, integrating experimental feedback to improve model performance.
• Analyse 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 optimisation, structure–performance analysis, and/or mechanistic investigations.
• Familiarity with machine learning techniques, optimisation algorithms, and data‑driven approaches for materials discovery and process optimisation.
• 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 organisational skills, along with strong written and verbal communication abilities.

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

Hiring Institution

NTU A research‑intensive public university, Nanyang Technological University, Singapore (NTU Singapore) has 33,000 undergraduate and postgraduate students in the Engineering, Business, Science, Humanities, Arts, & Social Sciences, and Graduate colleges. It also has a medical school, the Lee Kong Chian School of Medicine, established jointly with Imperial College London. NTU is also home to world‑class autonomous institutes – the National Institute of Education, S Rajaratnam School of International Studies, Earth Observatory of Singapore, and Singapore Centre for Environmental Life Sciences Engineering – and various leading research centres such as the Nanyang Environment & Water Research Institute (NEWRI) and Energy Research Institute @ NTU (ERI@N). Ranked amongst the world’s top universities by QS, NTU has also been named the world’s top young university for the past seven years. The University’s main campus is frequently listed among the Top 15 most beautiful university campuses in the world and has 57 Green Mark‑certified (equivalent to LEED‑certified) buildings, of which 95% are certified Green Mark Platinum. Apart from its main campus, NTU also has a campus in Novena, Singapore’s healthcare district. Under the NTU Smart Campus vision, the University harnesses the power of digital technology and tech‑enabled solutions to support better learning and living experiences, the discovery of new knowledge, and the sustainability of resources.

For more information, visit www.ntu.edu.sg

About NIE About RSIS About LKCMedicine About NTUItive