PhD Studentship available in the lab of Dr Richard Gill at Imperial College London. Working to develop remote sensing technologies to reveal pollination signatures in real-time.
- Deadline: 5th April, 12 noon
- Stipend: £17,609
- Start date: October 2021
- Duration: 3 years
Effective crop pollination is critical for sustainable, global food production. For instance, insect pollination benefits >70% of global crops, contributing >£150bn annually. Yet our capability to measure spatial and temporal distributions of pollination is limited. Developing tools to precisely reveal pollination signatures will enable assessment of pollination asynchronies and deficits across agri-landscapes, informing evidence-based action to enhance crop uniformity, yields and farming efficiency. This is especially true for assessing insect pollination, as current approaches reliant on physically walking through crops recording insect presence and flower drop are time consuming, expensive and prone to inaccuracies.
Our overarching goal is to develop remote sensing technologies to reveal pollination signatures in real-time, aligning with BBSRC’s innovation priorities of ‘transformative technologies’ and ‘sustainable agriculture’. We will explore molecular and chemical profiles of crop plants transitioning from flowering to seed-set, and integrate these with external spectral signals for detection through remote sensing; meeting the BBSRC challenge of ‘an integrated understanding of [plant] health’. Such a diagnostic toolkit will help precision farming strategies, such as movement of managed pollinators, optimised harvesting, safer pesticide applications and landscape management.
This studentship will focus on pollination responses in the globally important crop, oilseed rape. The student will undertake pollination assays (including the use of insect pollinator trials) where plant tissue samples will be taken alongside spectral imaging. In partnership with Agriculture & Horticulture Development Board (ADHB), the use of lab, mesocosm and outdoor facilities will allow comparisons between plants grown in controlled and field conditions. Using next-gen RNAseq, student will take a transcriptomics approach (using tissue samples) to investigate gene expression changes in response to pollination, and subsequently reveal gene network responses and identify ‘pollination’ hub genes. The student will then couple these findings with the spectral imaging dataset, to investigate changes in plant reflectance and reveal spectral signatures of pollination. Together, this will help us to associate molecular and phenotypic temporal responses, with the opportunity of using machine learning techniques on both datasets to identify reliable candidate bioindicators.
Using experimental and exploratory science, this project will contribute to a diagnostic tool-kit to develop remote sensing applications to improve farming. Thus, the work has real-world applications and the potential for huge impact. This multi-disciplinary project will take a heavy bioinformatics component involving the management of big data and development of analytical pipelines. Therefore, evidence of quantitative analytical skills, experience in coding and script writing, and a willingness to learn and commit to furthering these skills by applicants will be looked on favourably. The project supervisors are shared between Silwood Park (campus near Ascot ca. 20km from central London) and South Kensington (main campus in central London), but there is a lot of flexibility on how the student would like to work between these two campuses.
Originally published on the Imperial College London website: https://www.imperial.ac.uk/bbsrc-doctoral-training-partnership/application-process/2021-departmental-studentship-projects-/