Job Description

Topics

Computer Graphics, Applied Perception, Machine Learning, Optimization, Image and Video Quality

This postdoctoral position is offered in collaboration between the Graphics and Displays group at the University of Cambridge and our industrial partner, the Applied Perception Science team at Meta Reality Labs. The position is offered in the Department of Computer Science and Technology, University of Cambridge.

Our group is committed to a whole-system, multidisciplinary approach to computer graphics and perception research, with the opportunity to interact with both industry and academic collaborators and a strong focus on publishing and disseminating high-quality work at key conferences, such as SIGGRAPH and CVPR.

Our work lies at the intersection of visual computing and perception: understanding human vision is central to our research, as it informs work on displays, optics, rendering, and many key aspects of AR/VR systems. We are looking for an experienced researcher who will be responsible for supporting central lines of work. This involves surveying the field, setting priorities, and then conducting and helping lead research at the intersection of basic and applied science. Applicants will be expected to participate in technical discussions, help define project direction, and produce results that are novel, technically sound, and practically applicable.

The core goal of this project is to develop a deeper practical and theoretical understanding of robust perceptual representations, cost functions, and metrics for key visual computing applications in AR/VR. These techniques will account for the perceived degradation of visual quality while modeling the physical specification of the target display (e.g. display size and colour gamut). As a result of the project, we will provide means to improve the perceptual quality of VR/AR displays, rendering techniques, and AR/VR camera systems.

Minimum Qualifications

- Candidates should hold an advanced degree in computer science, electronic engineering, or a closely related discipline, with experience and interest in image and video quality. * Appointment at a research associate level requires a completed PhD (or equivalent experience).

• Where a PhD has yet to be awarded, an appointment can initially be made as a research assistant and amended to research associate after PhD completion.

- Experience with visual quality metrics (e.g. reference and no-reference).

- Comfortable with gathering and communicating findings through modeling, psychophysical experimentation & data analysis.

- Solid experience with machine learning and image processing. Sufficient experience with deep learning frameworks, such as PyTorch.

- Strong programming skills.

Desirable Qualifications

- Research published in high-impact journals and international conference presentations (SIGGRAPHs, Eurographics, Journal of Vision, CVPR, etc.)

- Familiarity with one or more low-level vision science fields (colorimetry, dynamic vision, spatiotemporal perception, depth perception)

- Experience predicting, quantifying, and resolving perceptual artifacts arising from novel displays based on technical specifications or architectures

- Familiarity with modern display technologies, subsystem interactions and perceptual tradeoffs, common display artifacts and mitigation strategies

- Technical familiarity with modern graphics algorithms and pipelines (e.g. imaging pipelines, HDR and tone mapping, foveated and perceptually-driven rendering, realism in graphics, physically-based rendering)

-Experience with hardware prototype design and experimentation, including test-beds and proof-of-concept devices

- Experience with deriving new metrics for novel use cases, including novel display architectures, graphics pipelines, or environment

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

\xc2\xa329,605 to \xc2\xa344,263 per annum

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