教授、博导、国家优青Professor · Ph.D. Advisor · NSFC Excellent Young Scientists Fund
北京师范大学人工智能学院School of Artificial Intelligence, Beijing Normal University
聚焦计算机视觉、计算成像、计算摄影与人工智能，探索视觉感知、成像系统与智能算法之间的协同设计。We study computer vision, computational imaging, computational photography, and artificial intelligence, with an emphasis on co-designing visual perception, imaging systems, and intelligent algorithms.
课题组重视扎实的问题定义、可复现的实验、可靠的协作以及真实世界影响，欢迎对视觉智能与成像技术有长期兴趣的申请者。We value clear problem formulation, reproducible experiments, reliable collaboration, and real-world impact. Applicants with sustained interests in visual intelligence and imaging are welcome.
博士、硕士、科研实习生与博士后长期开放Openings for Ph.D., Master's, research interns, and postdocs
联系时请附个人简历、成绩单、代表性论文或项目链接，并用简短文字说明研究兴趣与可投入时间。Please attach your CV, transcript, representative papers or project links, plus a short note on research interests and availability.
欢迎博士研究生、硕士研究生、科研实习生和博士后申请。申请者应具备计算机、人工智能、电子信息、自动化、数学、光学或相关专业背景；对计算机视觉、计算成像、计算摄影或人工智能有明确兴趣；具备扎实的数学基础与编程能力，熟悉 Python / PyTorch 者优先；能够阅读英文文献、复现实验、清晰记录过程，并在长期课题中稳定协作。已有高水平论文、开源项目、竞赛或科研经历者优先。Ph.D. students, master's students, research interns, and postdoctoral researchers are welcome. Applicants should have a background in CS, AI, EE, automation, mathematics, optics, or related fields; a clear interest in computer vision, computational imaging, computational photography, or artificial intelligence; solid mathematics and programming skills, preferably Python / PyTorch; and the ability to read literature, reproduce experiments, document work clearly, and collaborate reliably on long-term research. Strong papers, open-source projects, competitions, or research experience are a plus.
博士研究生Ph.D.
面向长期基础研究与高水平论文产出。For long-horizon research and strong publications.
硕士研究生Master
面向扎实训练、工程实现与科研入门。For rigorous training, implementation, and research growth.
科研实习生Intern
建议连续投入不少于 3 个月。Preferably available for at least three months.
博士后Postdoc
支持独立课题推进、学生指导与项目申请。Expected to lead research, mentor students, and support proposals.
Research
研究方向Research directions
CV
计算机视觉Computer Vision
物理机理驱动、低层视觉与图像恢复，关注复杂退化条件下的可靠视觉感知。Physics-driven and low-level vision, including reliable perception and image restoration under complex degradations.
CI
计算成像Computational Imaging
协同设计算法、相机与光学系统，突破传统成像在光谱、速度和动态范围上的限制。Co-designing algorithms, cameras, and optics to extend spectral, temporal, and dynamic-range capabilities.
CP
计算摄影Computational Photography
面向真实场景的图像恢复、重建与增强，提升成像质量和视觉信息表达能力。Image restoration, reconstruction, and enhancement for real scenes and more expressive visual information.
AI
人工智能Artificial Intelligence
围绕视觉与成像任务构建可解释、可复现、能泛化的智能方法。Interpretable, reproducible, and generalizable intelligent methods for vision and imaging tasks.
Lizhi Wang 王立志
Professor, Ph.D. Advisor
Intelligence Media Computing Lab, School of Artificial Intelligence
Opportunity! I currently serve as an editorial board member of IEEE Transactions on Image Processing (TIP). Our team is recruiting faculty members, postdoctoral researchers, and self-motivated Ph.D. and master's students interested in computer vision, computational photography, and image processing. Interested candidates are welcome to email me their CVs. [Chinese Homepage][Google Scholar]
对研究或加入团队感兴趣？Interested in our research or joining the team?
请以“申请类别－姓名－所在学校”为邮件主题，附个人简历、成绩单、代表性论文或项目链接，并简要说明研究兴趣与可投入时间。Use “Position – Name – Institution” as the email subject. Attach your CV, transcript, representative papers or project links, and briefly describe your research interests and availability.
