Research Fellow (AI for Materials Design)

National University of Singapore; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading research institution in Singapore is seeking passionate Research Fellows to innovate in materials science using AI. Candidates with a PhD and strong interdisciplinary skills are encouraged to apply. Responsibilities include developing AI models for material design and conducting experiments relevant to advanced materials. This opportunity offers a dynamic research environment aimed at transforming materials innovation.

Qualifications

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, or Mechanical/Aerospace Engineering.
• Strong publication record demonstrating creativity and domain expertise.
• Proven ability to work in interdisciplinary teams.

Responsibilities

• Develop AI models to predict and design materials.
• Perform simulations to model structural and thermodynamic properties.
• Collaborate with researchers for experimental and theoretical validation.

Skills

Machine learning

AI models

Interdisciplinary collaboration

Synthesis and characterization

Education

PhD in Materials Science or relevant field

Tools

XRD

TEM

SEM

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 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.

Theory & AI in Materials Design

• 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.

Experiments & AI Integration

• 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.