By 2022 there will be 45 billion cameras in the world, many of them tiny, connected and live streaming 24/7. Self-driving cars, drones and service robots are just three manifestations. The cameras may even capture video data beyond the visual spectrum. The embedded systems in which these cameras are integrated, come processors powerful enough to run video understanding algorithms founded on computer vision and deep learning. This is an invitation to move away from traditional video understanding domains, like broadcast news, television archives and social media. And, instead, emphasize hitherto non-mainstream video domains like surveillance, healthcare and robotics where viewpoints are new, labeled examples are scarce and real-time spatiotemporal understanding is crucial. All these developments open up exciting research avenues for video understanding.
The new tenure tracker is expected to contribute to fundamental research in video understanding. We anticipate that the field of video understanding combined with embedded cameras will drive the next wave of innovation in this field. The tenure tracker is expected to have a keen interest and expertise in one or more core video understanding areas.
The tenure tracker is expected to acquire his/her own independent funding from sources such as the national funding agency NWO (e.g. VIDI), EU funding via H2020 (e.g. ERC starting grant) and industry. In terms of teaching, the tenure tracker will contribute to strengthening the curriculum of the Bachelor and Master AI and related programs such as Computer Science. The teaching load is around 30%. The tenure tracker is expected to contribute to valorization, both in terms of engaging with the media as well as applying the state of the art research tools to applications in society. UvA spinoffs that bring technology to industry and society are highly encouraged.
Finally, the tenure tracker is expected to help with management of the Informatics Institute.
Full vacancy and application requirements here.