Principal/Senior Researcher - Video Standards
The Cloud & Smart Industries Group (CSIG) is responsible for promoting the company's cloud and industry Internet strategy. CSIG explores the interactions between users and industries to create innovative solutions for smart industries via technological advancements such as cloud, AI, and network security. While driving the digitalization of retail, medical, education, transportation and other industries, CSIG helps companies serve users in smarter ways, building a new ecosystem of intelligent industries that connect users and businesses.
We are inviting motivated researchers and engineers to join our team. The team works on developing cutting edge technologies in multimedia data compression, processing, transmission, analysis and beyond. Some example areas include (but not limited to) video compression algorithms, future video coding standards, learned image and video compression, VR and 360 video coding and transmission, intelligent visual data analysis and representation, future immersive data compression and processing (e.g. point cloud, light field, etc.).
- Research, design, implement, and optimize novel algorithms/models that lead to improved video compression performance in future or existing codecs
- Summarize the developed algorithms into academic papers, standard contributions and patent applications
- Represent company in standard meetings and academic conferences
- Master degree in computer science, electrical engineering, math, statistics, or related fields
- 5+ years of related working experience
- Publications at top-tier peer-reviewed conferences
- Interest and experience in video compression and video coding standards
- Strong coding skills in C/C++
- Ability to communicate/collaborate with other researchers/engineers
- Ability to think out of box
- Proven track record of making contributions to video coding standards
- Good understanding of state-of-the-art video compression algorithms
- Working experience on HEVC, VVC, VP9, AV1, AVS3
- Working experience on Joint Exploration Model by JVET
- Experience on learned image and video compression
