Driving sustainability and welfare in salmonid aquaculture via machine learning and causal inference frameworks.
Supervisors
Simon Babayan, School of Biodiversity, One Health and Veterinary Medicine, (University of Glasgow)
Fiona Henriquez, Department of Civil and Environmental Engineering, (University of Strathclyde)
Martin Llewellyn, School of Biodiversity, One Health and Veterinary Medicine, (University of Glasgow)
Industry Partner - Bakkafrost
Summary
Are you passionate about data science and want to make a real-world impact in sustainable aquaculture? This PhD project offers a unique opportunity to combine cutting-edge analytics with the vital goal of improving fish welfare in salmon farming. You’ll work closely with top industry players like Bakkafrost, Scottish Sea Farms, and MOWI, and become a key link between data scientists and aquaculture professionals.
In this role, you’ll dive into over 10 years of fish welfare and environmental data, applying advanced statistical tools like Structural Causal Models (SCM) to understand and predict complex gill disease (CGD) and other welfare issues. Your work will go beyond data mining—you’ll create user-friendly platforms in R Shiny or Tableau to help aquaculture staff make data-driven decisions in real-time.
Not only will you gain proficiency in handling vast datasets and cutting-edge modeling, but you’ll also be at the forefront of revolutionizing how fish welfare is monitored, potentially affecting millions of fish. This project is more than academic—it’s a chance to shape the future of sustainable aquaculture, addressing key challenges like climate change while improving both productivity and fish health. If you're ready to make a tangible difference, this is the project for you.