Fixed-term: The funds for this post are available for 1 year.
We are seeking a computer scientist with experience in both embedded systems and/or sensing systems and AI algorithms, who is interested in working at the interface between computer science and zoology for a project on biodiversity monitoring. They will join the Energy and Environment Group in the Department of Computer Science and Technology and collaborate closely not only with faculty and students in the Department of Zoology and the Department of Plant Sciences but also Nokia Bell Laboratories, Cambridge, a leading lab working in the areas of Future Devices, Mobile Sensing and Systems, Embedded Machine Learning, and Internet of Things research.
The candidate will be part of the Cambridge Centre for Landscape Regeneration https://www.clr.conservation.cam.ac.uk/ (CLR), funded through the Natural Environment Research Council's Changing the Environment programme. CLR aims to provide the evidence needed to the UK government to fulfil its ambitions to bring back more nature to British countryside and deliver more ecosystem services than is current the case. The programme is focusing initially on two contrasting landscapes: the East Anglian fenland (primarily used for productive agriculture) and the Scottish Highlands (traditionally used for deer stalking and forestry plantations).
The project aims to design and implement an ultra-low power ML system on energy-autonomous devices (camera, microphone, and low-fidelity sensors) for biodiversity monitoring (especially insects). The project will leverage existing energy-autonomous devices, such as Nokia Bell Labs camera battery-less camera prototype.
(https://dl.acm.org/doi/10.1145/3384419.3430782)
Preference will be given to candidates with experience in sensors, embedded systems and low-power ML algorithms, with some experience with field data capture and a keen interest in applying computer science to conservation issues.
Essential
An MPhil in Computer Science
A good understanding of power-constrained DNNs
Practical experience in designing and developing algorithms for biodiversity monitoring
Willingness to work in a large interdisciplinary team addressing practical problems
Good communications skills
Desirable
Motivated by an interest in conserving nature
Knowledge of Tsetslin machines
Willingness to engage in some biodiversity field work
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