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PhD opportunity: Develop a framework using Machine Learning (ML) to identify specific features of stress response using plant phenotyping
Background: Early stress detection in crop plants is highly relevant for food security but methodologically complex. Plant stress responses can be observed using non-invasive methods such as plant imaging. The use of different camera sensors such as RGB, hyperspectral and chlorophyll fluorescence enable the detection of altered plant performance in response to environmental stress. Such changes include slower growth, senescent changes in the spectral reflectance and modified fluorescence responses of leaves. Although these changes cannot be captured by human eye, the imaging sensors used in this project can perceive such changes very fast. To understand the early responses of plants to environmental stress, we will develop a framework of data collection, storage and curation, trait extraction and utilization of ML and classification algorithms to extract the features from plant images. Plant images will be collected using imaging platforms in a controlled environment and unmanned aerial vehicles in field conditions(UAVs).
Project: This exciting project will develop a specific framework that utilizes concepts from probability theory, statistics, decision theory, optimization and visualisation to identify specific features of stress response using thousands of plant images. The Ph.D. student will have the opportunity to participate in training courses, workshops and conferences. The research will be supervised by Assoc. Prof. Eleni Mangina from UCD- School of Computer Science and Assist. Prof. Dr. Sónia Negrão (UCD- School of Biology and Environmental Science). This Ph.D. project is part of the SFI President of Ireland Future Research Leaders Award lead by Principal Investigator for this research programme Dr. Sónia Negrão at the School of Biology and Environmental Science, University College Dublin.
Details: This is a 4-year full-time position, with a stipend of €18,500 per annum.
Qualifications: Candidates should hold bachelor’s degree in computer science or related field (preference will be given to master’s degree), with some previous experience in data analysis, and have an interest in machine learning. Experience in plant-imaging with either high-throughput platforms and/or unmanned aerial vehicles (UAVs) would be desirable but not mandatory. Candidates must demonstrate an awareness of equality, diversity and inclusion agenda.
Application process: Informal enquiries are welcome and should be directed to Assoc. Prof. Eleni Mangina (email@example.com) and Assist. Prof. Dr. Sónia Negrão (firstname.lastname@example.org). Applications including a detailed CV, letter of motivation (max 2 pages, single spaced), transcripts and two letters of recommendation (to be sent directly from referee) should be sent to email@example.com no later than 30th of May 2020.