Research Assistant £25,298 - £29,301 or Research Associate £29,301 - £38,183
Fixed-term: The funds for this post are available for 32 months.
We are looking for a highly motivated Research Assistant/Associate with an interest in machine learning and human visual perception. The project, a collaboration with Apple Inc., will combine methods from psychophysics and vision science with modern machine learning to develop new models of human vision, which will enable intelligent display algorithms, adaptable to visual perception.
The successful candidate will be responsible for modelling, machine learning, display algorithm design, and work with prototype high dynamic range displays. They will spend two secondments, one month each, in the Psychological Science department of the University of Liverpool to facilitate cross-disciplinary training and joint work on the project.
The successful candidate will have the opportunity to work on real-life applications, e.g. the adjustment of image appearance depending on the user's visual performance (age-adaptive rendering). The partnership with Apple will provide the research associate with a unique opportunity to explore links outside of academia.
This project presents a unique training opportunity for the candidate who will benefit from experts working on the same topic (colour appearance) from different theoretical backgrounds (Rafal Mantiuk, Computer Vision - University of Cambridge; Sophie Wuerger, Human vision - University of Liverpool). The project will be advised by Graham Finlayson (University of East Anglia), Jasna Martinovic (Aberdeen) and Garrett Johnson (Apple Inc.).
This project will investigate human perception in the context of novel high dynamic range display technologies. It involves psychophysical experiments as well as modelling spatio-chromatic appearance under a wide range of light levels, from mesopic light levels to the levels produced by high-dynamic range displays.
Essential Skills: This position can be filled by an appropriate candidate at research assistant or research associate level, depending on relevant qualifications and experience. Appointment at research associate level is dependent on having a PhD (or equivalent experience). Where a PhD has yet to be awarded appointment will initially be made as a research assistant and amended to research associate when the PhD is awarded.
-PhD awarded or near completion in Computer Science, Engineering or Vision Science (Psychology) in one of the broad areas: computer vision, computer graphics, machine learning, human vision. -Good track record of publications in high esteem journals and conferences. -Excellent programming and mathematical skills. -Good communication skills in both written and spoken English. -Track record or interests in human vision modelling.
Desirable Skills: -Background in one or more specialized areas: machine learning, statistical inference, statistical methods for psychophysics, colour appearance, high dynamic range imaging, human visual perception. -Experience in psychophysical methods. -Skills in working with large quantities of data.
Candidates are strongly encouraged to create a Google Scholar or ORCID profile, link all publications and provide the link to the profile in their application. We also encourage links to OpenSource projects or datasets that the candidate has authored.
This position has been re-advertised. The candidates who applied in the first round are asked not to apply unless they have been invited to do so.
Start date: 1 April 2017 (flexible).
To apply online for this vacancy, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
Please ensure you upload your Curriculum Vitae (CV) and a covering letter along with the relevant links. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Please quote reference NR11391 on your application and in any correspondence about this vacancy.
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