UofG and Leverhulme Trust unveil new ecological data science PhD scholarships
Published: 2 February 2024
New funding from the Leverhulme Trust is set to support the creation of new fully-funded PhD opportunities at the University of Glasgow, including places targeted at people from underrepresented groups and international students.
New funding from the Leverhulme Trust is set to support the creation of new fully-funded PhD opportunities at the University of Glasgow, including places targeted at people from underrepresented groups and international students.
A £2.2m grant from the Leverhulme Trust will support a total of 18 new PhD students to work at the intersection of ecology and data science at the University’s Colleges of Science & Engineering and College of Medical, Veterinary & Life Sciences.
The Leverhulme Programme for Doctoral Training in Ecological Data Science will recruit five PhDs per year for the next three years, with the first cohort set to start in October 2024. Each successful applicant will also receive a grant of £10,000 for research and training expenses during their PhD.
Three of the PhD scholarships will be open specifically to students from East Africa, where University of Glasgow researchers have built strong research links with local and regional institutions over recent decades.
The funding also includes ring-fenced support for three additional Master’s Plus positions for UK students with Black or mixed-race heritage and/or those from low-income backgrounds. The Master’s Plus programme will support students to complete an MSc course by providing tuition fees and a stipend, followed by entry to the PhD programme.
Four-year PhD opportunities are currently available with researchers from the University’s School of Mathematics & Statistics and School of Biodiversity, One Health & Veterinary Medicine.
The projects seeking to recruit PhD students harness the latest techniques in AI, machine learning and modelling to gain new insights on a range of ecological sciences.
Several projects involve understanding animal behaviour and interactions, from modelling fish schools to examining how human presence impacts wildebeest herds in the Serengeti.
Other initiatives focus on critical environmental challenges like leveraging models and data to generate useful ecological predictions amidst a changing climate. There are also opportunities to use genomic sequencing to reconstruct disease outbreaks and characterise ecological communities.
Professor Colin Torney, of the University of Glasgow’s School of Mathematics and Statistics, led the bid to secure the funding for the scholarships.
He is seeking to recruit PhD students to work a project which will find new methods of modelling the movement of animal groups using data gathered from drone footage.
“Machine learning and artificial intelligence, combined with new technologies that enable large-scale surveillance of animal populations, are opening up many new avenues for researchers. The Leverhulme Programme for Doctoral Training in Ecological Data Science will help create researchers capable of making important breakthroughs in this area in the years to come.
“I’m particularly pleased that the funding includes new opportunities for international students from countries including Tanzania and Kenya. The University of Glasgow has a long history of valuable research partnerships with institutions in East Africa, and these scholarships will help us build on that strong foundation of knowledge exchange .
“I’m looking forward to welcoming the first cohort of Leverhulme Trust Doctoral Scholars when they start in Glasgow later this year.”
The scholarships are the latest in an ongoing series of developments aimed at widening participation in higher education at the University of Glasgow.
The James McCune Smith Scholarship programme, launched in 2022, has helped dozens of Black UK domiciled students to undertake PhD research at the University of Glasgow.
For more information on the scholarships, or to apply, visit the Leverhulme Programme for Doctoral Training in Ecological Data Science web pages.
First published: 2 February 2024
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