A strong background in deep learning, with publications in top ML conferences, e.g., NeurIPS, ICLR, ICML.
Master degree or above.
Passion in science related problems, including but not limited to, drug discovery, biology, and materials science.
Willingness to join a large team, work on a big project, and generate big impact through collective efforts and team collaboration.
Proficiency in Python and relevant ML libraries (e.g., PyTorch).
Experience with transformer-based models (e.g., large language models (LLMs) like GPT, Llama), or expertise in reinforcement learning or generative models (e.g., diffusion models, flow-matching).
Applicants should be fluent in both spoken and written English. They must demonstrate their ability to conduct solid research in machine learning, as evidenced by high-quality publications or a proven track record of innovation. While a background in biology, drug, or materials is advantageous, it is not mandatory. Candidates must be able to collaborate effectively with other researchers, and we are particularly interested in those who can work across disciplinary boundaries.