Join us

Welcome to Prospective Students

We focuses on multimodal language models, embodied intelligence, cognitive-inspired neuromorphic computing architectures, and advanced software-hardware co-design. Our work is characterized by strong interdisciplinary collaboration and deep integration of software and hardware innovations.

We maintain active research collaborations with leading institutions worldwide, including the MIT, Georgia Tech, UIUC, Boston University (BU), University of British Columbia (UBC), University of Florida (UF), as well as top Chinese universities and research centers such as Tsinghua University, Peking University, Zhejiang University, and the Chinese Academy of Sciences.

Our lab offers excellent research facilities and sustained funding support. We warmly welcome motivated and self-driven students to contact us.

Welcome to Prospective Interns

We welcome talented and dedicated students to join us. Prospective interns are expected to have a solid foundation in machine learning and deep learning prior to applying. To help you prepare, we provide a recommended course list and timeline designed to help you acclimate to our research pace:

• ML (1 week): https://www.coursera.org/learn/machine-learning
• DL (3 weeks for Lecture & 2 weeks for Assignments):
  https://cs231n.stanford.edu/index.html

We also encourage applicants to gain hands-on experience in deploying and fine-tuning LLMs. Most importantly, we emphasize that data is the most fundamental asset behind any successful model—high-quality data collection, curation, and utilization lie at the heart of impactful research.

The following resources may support your preparation and practical exploration in these areas(click to start!):

• LLaMAFactory: A popular open-source tool for fine-tuning and deploying LLMs
• Google Colab: An online coding platform for cloud-based experiments, ideal for free GPU/TPU trials
• Transformer Explainer: Visual tool to explore how Transformers work
• LLM Resources by Ben Bycroft: A curated collection of high-quality notes on LLM practice and theory
• YouTube Video: Hands-on tutorial for large language models

Contact

For perspectives students: boranzhao@xjtu.edu.cn

For perspectives interns: andycui@stu.xjtu.edu.cn