Thank you for your interest in our group! We use an internal database to manage all applications to ensure a speedy and fair evaluation process. All applicants (regardless of type of position) should use this form to submit an application. The evaluation process usually takes place in four stages:
- Preliminary review of submitted application materials. This usually takes place within two weeks of your submission.
- Initial interview via Zoom. We invite a subset of applicants for an initial interview with Prof Ong and his postdoctoral associates. In this initial interview, you will be requested to prepare a short 10-slide/10-min presentation summarizing your research experience, what you hope to get out of your time and what you feel you would be able to bring to our lab. You will be informed of the results of this initial interview within a week.
- Full interview. We invite you for a 2-hour interview with the whole group. You will be requested to give an hour-long presentation that outlines your research accomplishments and future plans in greater detail. Conditions permitting, this interview will take place in person and all travel expenses will be covered. In the event this is not possible, the interview will take place via Zoom. This interview is also an opportunity for you to get to know the group members and ask any questions you may have.
Before submitting an application, it is important to review the research vision and mission of our group as well as its guide to make sure it is compatible with your career goals and experience. While we strive to be an inclusive, nurturing and exciting lab, we recognize that not everyone necessarily has the same interests.
Open Positions
Prof Shyue Ping Ong is building the Materialyze.AI Lab in the Department of Materials Science & Engineering at the National University of Singapore (NUS). We are seeking multiple postdoctoral associates and graduate students to join our highly dynamic team starting January 2026.
Our lab pioneers the integration of theory, experiments, and AI to accelerate the discovery and deployment of breakthrough materials. Current application areas include next-generation batteries, aerospace alloys, and semiconductors, though we are highly flexible in our technological areas of interest. We welcome applicants with expertise in either theory & AI or experimental materials research, or ideally, a combination of both.
Research Tracks
Theory & AI in Materials Discovery
- Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design novel 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.
Experimental Materials & 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.
General Responsibilities
- Lead independent and collaborative projects at the interface of computation, AI, and experiment.
- Publish research findings in reputable journals and present at international conferences.
- Contribute to the training and mentorship of graduate and undergraduate researchers.
Qualifications
- For Postdoctoral Applicants: Ph.D. (or expected completion by start date) in Materials Science, Physics, Chemistry, Chemical Engineering, Computer Science, or related disciplines.
- Track record of research excellence demonstrated through publications in leading journals.
- For the Theory/AI track: Strong expertise in computational modeling (DFT, MD, MLIPs) and/or AI/ML methods; proficiency in scientific programming (e.g., Python, PyTorch/TensorFlow, HPC).
- For the Experimental track: Strong expertise in materials synthesis and characterization; prior experience in automated labs, data-driven experimentation, or integration with computational/AI workflows is highly desirable.
- Excellent communication skills and ability to work in an interdisciplinary, collaborative environment.
Why Join Us?
✅ Be part of a world-leading research environment at NUS.
✅ Collaborate with international partners across academia and industry.
✅ Access cutting-edge computational and experimental facilities.
✅ Competitive salary and benefits, commensurate with experience.
Application Process
Interested candidates should submit the following at this form:
1. A cover letter indicating whether you are applying for the Theory/AI track, the Experimental track, or both.
2. Curriculum vitae, including a list of publications.
3. Contact information for at least three referees.
4. (Optional)
Applications will be reviewed on a rolling basis until positions are filled.