Our PhD and MPhil positions are full, and we are not recruiting for any upcoming admission cycle. We do not expect PhD or MPhil recruitment to resume before Fall 2029. We are also not recruiting research assistants (RAs) or postdoctoral researchers at present.

A personal note. Our research involves difficult problems that need sustained attention and careful, hands-on work. I still write much of the code myself, alongside supervising our current projects. That leaves limited time to train and mentor new people. I have therefore decided to keep the group small and focus on supporting the people already here.

We receive many thoughtful applications from strong candidates, and turning people away is the hardest part of this decision. If you have taken the time to write to us, thank you for your interest and effort. Our limited capacity is not a judgment of your ability. I hope you find a group where you can pursue your ideas with the time and support you need.

Our group is affiliated only with HKUST's Department of Chemistry and Department of Chemical and Biological Engineering (CBE). When PhD and MPhil recruitment resumes, admission will be through Chemistry or CBE. We are not affiliated with CSE, ECE, or Physics.

HKUST undergraduate and master's students may still undertake research-credit projects with us through UROP, FYP, or master's research projects. The HKUST UROP, FYP, & Master Research page lists our current active projects.

If your research interests are closely aligned with ours, you may still write to us with any of the materials below. This does not change the recruitment status above, and we may not be able to reply to every message.

• Curriculum Vitae (CV)
• Academic transcript
• Research proposal
• Representative publications or other research outputs
• Expected starting date

The following courses and topics are helpful for understanding the core research of the AI for Physical Sciences Lab. You may also check the Mini Tests to explore your interests.

1. CS/Math: Linear Algebra, Probability and Statistics, Data Structures, Introduction to Machine Learning/AI, Mathematical Modeling, Stochastic Processes and Markov Chains, Linear Programming, Convex Optimization, Statistical Learning Theory, Information Theory, Functional Analysis, Numerical Analysis/Linear Algebra/PDE, Stochastic DE, Differentiable Manifolds, Lie Algebra, Computational Complexity, Topics in ML (Gaussian Processes, Graph Neural Networks, Reinforcement Learning, Generative Models, Language Models). Abstract Algebra, Topology, and Computational Graphics are not required but are a plus.
2. Physics/Chemistry/Materials: Quantum Mechanics, Statistical Mechanics, Atomic Physics, Electronic Structure Theory, Computational Physics, Group Representation Theory (note: not just group theory; chemistry students may encounter this in inorganic or structural chemistry courses), Solid-State Physics, Quantum Field Theory, Polymer Chemistry/Physics, Introduction to Quantum Computing.
3. Other foundational skills: GitHub (open-sourcing spirit), Python (NumPy, SciPy, Scikit-learn, Pandas, etc.), PyTorch, JAX. Proficiency in using the Linux operating system, command line, Slurm, Docker/Conda for environment setup, and the ability to collaborate effectively with AI agents (e.g., vibe coding, automation pipeline for your Gaussian calculations, auto-matching paper formulas with code, etc.). C++ and Julia are not required but a plus. Java is not required and not a plus.