PhD Studentship: Machine learning algorithms for detecting dangerous mosquito species
University of East Anglia - Computing Science
|Funding for:||UK Students, EU Students|
|Funding amount:||£14,296 pa|
|Placed on:||17th October 2016|
|Closes:||28th November 2016|
Start Date: 1st October 2017
No. of positions available: 1
Supervisor: Dr Anthony Bagnall
Project description: One of the most significant challenges in the combat against vector based infectious diseases such as malaria, dengue and zika is the lack of extensive information as to the locations and dynamics of the populations of the disease-bearing mosquitoes. If we were able to automatically detect the presence of different species of mosquitos it could transform our ability to tackle these diseases. Detecting sensors could be used to track the effectiveness of preventative measures, act as early warning devices for population growth in dangerous species and provide long term data on changes in abundance.
Unfortunately, insect surveillance is time consuming and expensive with current technology. However, recent advances in sensor technology  have indicated that it is possible to differentiate species by their sound in flight. Researchers at the University of California, Riverside (UCR), have built inexpensive devices that can detect flying insects passing through the sensor and use photonics to recreate the sound of the insect. This project involves extending recent advances in time series classification  and speech processing  to develop algorithms that can accurately classify the species of mosquito based on its sound. There will be opportunities of placements in California and Kenya to collaborate with researchers working in this area.
- Y. Chen, A. Why, G. Batista, A. Mafra-Neto, E. Keogh. Flying Insect Classification with Inexpensive Sensors. Journal of Insect Behavior: 27(5). 657-677, 2014
- A. Bagnall, J. Lines, J. Hills and A. Bostrom. Time series classification with COTE. IEEE Trans. KDE: 27 (9). 2522-2535, 2015.
- P. Harding and B. Milner. Reconstruction-based speech enhancement from robust acoustic features. Speech Comm.: 75. 62-75, 2
Person specification: Minimum entry 2:1
Full Studentships cover a stipend (RCUK rate: £14,296pa – 2016/7), research costs and tuition fees at UK/EU rate, and are available to UK and EU students who meet the UK residency requirements.
Students from EU countries who do not meet the UK residency requirements may be eligible for a fees-only award. Students in receipt of a fees-only award will be eligible for a maintenance stipend awarded by the NRPDTP Bioscience Doctoral Scholarships, which when combined will equal a full studentship. To be eligible students must meet the EU residency requirements. Details on eligibility for funding on the BBSRC website:
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South East England