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Understanding and monitoring insect pest dispersal within a changing environment

Research Projects

, Wednesday, January 13, 2021 Competition Funded PhD Project (UK Students Only)

About the Project

This exciting PhD project will work at the cutting edge of mathematical and statistical sciences to deliver automatic insect pest detection. Whilst the focus is on developing and applying data science techniques, you will also seek to understand complementary disciplines in biomechanics, insect behaviour and phenology.

You will develop a “digital twin” for the flight of insect pests by integrating state-of-the-art statistical methods with data obtained from opto-acoustic sensors, high-speed cameras and video tracking technology to associate audio signals with insect morphology. This digital twin will sit in the interface between statistics, mathematical biology and the life sciences and will be used to investigate how changing flight behaviour impacts detectable audio signals. Throughout the project, you will explore which environmental and biological factors are causing changes in flight behaviour and how this impacts the performance of machine-learning algorithms for insect classification. You will investigate methods for upscaling insect detection models for deployment in field. This research will directly contribute to the ongoing fight against vector-borne agricultural diseases, aiming to improve worldwide food security.

You will be working as part of multi-disciplinary team crossing the mathematical, statistical, ecological and biomechanical sciences. The PhD will largely be based at Rothamsted Research (Harpenden) with regular travel to and from University of Lancaster and occasional travel to Bangor University.

Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in a quantitative subject such as mathematics or statistics. Applicants will be considered if they hold a degree in a subject such as biology or ecology with a strong quantitative component.

Candidates should, ideally, have some experience in mathematical/statistical modelling and a willingness to undertake experiments. An interest in natural history and entomology would be an advantage.

For further details and informal queries, please contact Dr Kirsty Hassall ().

Funding Notes

Each student will be supported by the tax free stipend (£15,285 in 2020/21), for 3.5 years. The award will also cover UK home fees and a Research and Training Grant of £8,750 to fund their research, conference attendance and networking.

For more detailed eligibility criteria please see the Envision website - View Website.

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