Research Fellow (AI for Materials Design) 1

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28AI-for-Materials-Design%29-1/33649-en_GB/

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.