# Postdoctoral and PhD opportunities at Prof Shyue Ping Ong's New Materialyze.AI lab at NUS

**URL:** <https://accelerated-discovery.org/t/postdoctoral-and-phd-opportunities-at-prof-shyue-ping-ongs-new-materialyze-ai-lab-at-nus/572>\
**Category:** Job Board\
**Created:** [October 10, 2025, 8:11pm UTC](https://accelerated-discovery.org/t/postdoctoral-and-phd-opportunities-at-prof-shyue-ping-ongs-new-materialyze-ai-lab-at-nus/572 "2025-10-10T20:11:12Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![ongsp](https://yyz1.discourse-cdn.com/flex003/user_avatar/accelerated-discovery.org/ongsp/32/333_2.png) [@ongsp](https://accelerated-discovery.org/u/ongsp)\
**Post date:** [October 10, 2025, 8:11pm UTC](https://accelerated-discovery.org/t/postdoctoral-and-phd-opportunities-at-prof-shyue-ping-ongs-new-materialyze-ai-lab-at-nus/572/1 "2025-10-10T20:11:12Z")

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## General Information

**Position** : Postdoctoral Associate or PhD  
**Organization** : Materialyze.AI, National University of Singapore  
**Location** : Singapore  
**Type** : Full-time  
**Remote Work** : Hybrid

## Job Description

Prof Shyue Ping Ong is launching an exciting Materialyze.AI Lab at the National University of Singapore (NUS) starting **Jan 2026**. We are building a well-funded interdisciplinary team at the frontier of theory, experiments, and AI to accelerate the discovery of breakthrough materials for next-generation batteries, aerospace alloys, and semiconductors, though we are highly flexible in our application areas of interest.

We are recruiting in two tracks:  
🔹 Theory & AI – Develop and apply AI/ML models, interatomic potentials, and large-scale simulations to design new materials.  
🔹 Experimental – Synthesize and characterize functional materials, develop high-throughput workflows, and help build automated labs integrated with AI.

## Qualifications

- For Postdocs: 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.

## Compensation and Benefits

✅ Competitive salary & benefits

## Application Process

📩 Apply by submitting a cover letter, CV, and 3+ referee contacts via this [Google Form](https://lnkd.in/gep8D4qk).