Join Our Team: Pioneering the Future of Bayesian Inference and Neuromorphic Computing

Hello! I am Dan Yao, and I am excited to connect with potential collaborators.

My research focuses on Bayesian Inference, Spiking Neural Networks (SNNs), and Computational Imaging. We bridge the gap between rigorous probabilistic modeling and real-world perception, pushing the boundaries of technologies like single-photon LiDAR (SPAD) and Event-based Vision (EVS). Our mission is to develop scalable, efficient Bayesian algorithms that solve the most complex inverse problems in high-dimensional signals.

Are you ready to tackle the next generation of visual intelligence?

We are seeking highly motivated PhD students to drive research in the following core areas:

• Next-Gen Bayesian Learning: Advancing the efficiency and scalability of Variational Inference and Expectation Propagation.
• Neuromorphic Intelligence: Building novel training frameworks and sensing architectures for Spiking Neural Networks (SNNs).
• Computational Imaging: Leveraging model-driven deep learning to overcome bottlenecks in SPAD array imaging and high-speed 3D reconstruction.

Why Join Us?

You will be part of an intellectually rigorous, highly collaborative environment where we value both mathematical depth and practical innovation. We provide a space where ambitious ideas are encouraged, and your contributions will have a tangible impact on the future of autonomous perception and signal processing.

How to Apply:

Please reach out to me at yaodan@opt.ac.cn or yaodan080295@gmail.com with:

1. Your CV.
2. A brief statement outlining your research interests and why you are a good fit for our team.