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.

We are looking for a hands-on Research Fellow whose role will focus on integrating an agentic platform for electrocatalyst discovery and developing novel machine-learning algorithms (e.g., reinforcement learning) for materials discovery and process optimization. The candidate will work closely with a multidisciplinary team and ship end-to-end, closed-loop workflows that connect instruments, edge devices, data backends, and AI agents.

Key Responsibilities:

• Integrate instruments and IoT devices with reliable drivers/APIs; build autonomous/robotic testing workflows.

• Design LLM agents for planning, tool-use, and experiment control with safety interlocks.

• Develop and deploy RL/agentic active learning for closed-loop optimization.

• Build data pipelines, dashboards, and provenance; analyse and present results.

• Publish in peer review Journals and conferences , and coordinate with sponsors/collaborators.

Job Requirements:

• Ph.D.  in CS/EE/Robotics/ChemE/MechE or related.

• Strong Python; experience with hardware and software integration (serial/TCP/IP, MQTT, API).

• Practical ML background, including RL/BO; Docker/Linux/Git familiarity.

• Track record in automation or autonomous labs; good communication and collaboration skills.

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

Hiring Institution: NTU