Research Fellow (AI for Materials Design)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 50,000 - 80,000; Full time

Job summary

A leading university in Singapore is seeking a motivated Research Fellow for its Materials Science and Engineering department. The role involves developing AI models, conducting simulations, and collaborating on innovative materials research. Ideal candidates should hold a PhD and demonstrate a strong publication record, with hands-on experience in both experimental and theoretical domains.

Qualifications

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, or a related field.
• Strong publication record demonstrating creativity and rigor.
• Hands-on experience with synthesis and characterization equipment.

Responsibilities

• Develop and apply machine learning and AI models to predict and design materials.
• Perform first-principles and molecular dynamics simulations.
• Synthesize and process functional materials relevant to batteries and aerospace alloys.

Skills

Machine learning

AI modeling

Experimental synthesis

Interdisciplinary collaboration

Education

PhD in Materials Science or related field

Tools

DFT

Molecular dynamics

XRD

TEM

SEM

Spectroscopy

Electrochemistry

Job description

Interested applicants are invited to apply directly at the NUS Career Portal.

Your application will be processed only if you apply via the NUS Career Portal.

We regret that only shortlisted candidates will be notified.

Job Description

Prof Shyue Ping Ong’s Materialyze.AI lab at the Department of Materials Science and Engineering aims to pioneer the integration of theory, experiments, and AI to accelerate the discovery and deployment of breakthrough materials. We are recruiting highly motivated Research Fellows who are passionate about accelerating materials innovation through scientific rigor, creative thinking, and interdisciplinary collaboration. We welcome applicants with expertise in materials theory, experiments, AI for materials, or—ideally—a combination spanning these domains.

• Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials.
• Perform first-principles and molecular dynamics simulations to model structural, thermodynamic, and electronic properties.
• Contribute to open-source software, benchmarks, and datasets that advance the global materials community.
• Synthesize and process functional materials relevant to batteries, aerospace alloys, and semiconductors using solid‑state, solution, or thin‑film methods.
• Apply advanced characterization techniques (XRD, TEM, SEM, spectroscopy, electrochemistry, etc.) to probe structure–property relationships.
• Collaborate with theory and AI researchers to validate predictions, generate datasets, and develop high-throughput/automated experimental workflows.
• Experience in developing autonomous laboratory systems is a strong plus.

Qualifications

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Mechanical/Aerospace Engineering, or a related field.
• Strong publication record demonstrating creativity, rigor, and domain expertise.
• Proven ability to work in interdisciplinary teams.
• For experimental applicants: hands‑on experience with synthesis and characterization equipment.
• For theory/AI applicants: experience with DFT, MD, MLIPs, or AI/ML frameworks.