Leverhulme Programme for Doctoral Training, University of Glasgow,, Scotland

Expires on: 03/22/2024


Embark on a transformative journey with the Leverhulme Programme for Doctoral Training in Ecological Data Science at the University of Glasgow. Funded by the Leverhulme Trust, this program aims to train a new generation of data scientists equipped to address pressing environmental challenges. Students will delve into areas such as biodiversity loss, ecosystem degradation, and emerging infectious diseases, applying cutting-edge data science techniques to ecological and environmental issues.

The program’s interdisciplinary focus spans ecology, data science, and conservation, offering students the chance to work on diverse projects, including marine acoustic analysis, natural language processing for biodiversity, machine learning in metagenomics, statistical modeling of species distributions, and edge machine learning for animal monitoring.

Successful applicants will undergo a 4-year PhD program structured in two stages. The first stage involves training and rotation projects, while the second stage focuses on individual PhD research. The program emphasizes interdisciplinary collaboration, ensuring students work with world-leading researchers and undertake training in data science, ecology, and professional skills development.


  • Minimum 2.1 undergraduate degree in a relevant subject.
  • Evidence of quantitative skills for ecology applicants or an interest in ecology for quantitative backgrounds.
  • Preferred: Masters level qualification.


  • CV
  • Quantitative skills evidence or ecology interest statement
  •  Undergraduate and, if applicable, Masters transcripts

Support: Each scholarship funds up to 48 months of full-time doctoral study, including a maintenance award at UKRI base levels and £10,000 for individual research and training needs.

Over the program’s duration, 3 scholarships may be offered to international students, with an additional 3 scholarships available to students from East African Community states.

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