Openings

We welcome enquiries from self-motivated researchers interested in robust machine learning, AI safety, computer vision, and learning from imperfect information.

Prospective PhD Students

Students with strong programming skills and a solid mathematical or statistical foundation are encouraged to get in touch, especially if their interests overlap with:

learning with noisy, coarse, or incomplete supervision;
AI safety, certification, and robust evaluation;
foundation models for visual and multimodal data.

Visitors and Postdocs

Researchers with overlapping interests and their own fellowship, scholarship, or visiting support are welcome to discuss collaboration opportunities.

short-term visits and research exchanges;
joint papers on robust learning and AI safety;
collaborative projects across machine learning and computer vision.

Funding availability changes over time. Specific funded opportunities will be announced here when available.

How to Apply

Please email Dr. Feng with the subject line [Prospective Student/Visitor] Your Name - Your Current Institution. Include the following materials:

an up-to-date CV;
transcripts for BSc and MSc degrees, where applicable;
a brief research statement explaining your interests and fit with the lab;
links to papers, code, or research projects, if available.

Email Dr. Feng Review Research Areas