Application Deadline: 22 August 2025
Details
The Birmingham Institute for Sustainability & Climate Action (BISCA) is dedicated to fostering world-leading, transdisciplinary research and driving impactful solutions for a sustainable future. Our PhD studentships are designed to develop the next generation of research leaders committed to tackling complex global challenges. We bring together expertise from across disciplines, fostering an inclusive and collaborative research environment focused on real-world impact and meaningful climate action.
Top-level summary: This interdisciplinary PhD project will apply advanced Artificial Intelligence (AI) techniques to environmental and biodiversity datasets to develop a predictive framework for biodiversity loss in freshwater ecosystems. It aims to build a biodiversity-focused digital twin that simulates how freshwater ecosystems respond to multiple pressures over time. The successful candidate will design a user-friendly analytical dashboard enabling stakeholders to test land-use and restoration scenarios, thereby supporting evidence-based conservation planning and influencing nature-positive policy.
Project Highlights:
Develops an AI-powered biodiversity digital twin using environmental DNA (eDNA) to simulate ecosystem responses to pressures.
Aims to forecast biodiversity loss in freshwater ecosystems under climate change and chemical pollution scenarios.
Designs a user-friendly decision-support dashboard for stakeholders to test land-use and restoration scenarios.
Creates scalable, transferable tools that leverage AI for sustainability and climate action, with real-world impact.
Brief Overview: One of the major scientific challenges in sustainability is our limited ability to predict how climate change and chemical pollution affect biodiversity, threatening ecosystem services such as clean water, food security, and climate regulation. This project combines Artificial Intelligence (AI) and environmental DNA (eDNA) data to develop a biodiversity-focused digital twin that simulates how freshwater ecosystems respond to multiple pressures over time. This research directly aligns with BISCA’s mission to leverage AI for sustainability and climate action, developing digital tools for ecosystem modelling and informing environmental decision-making. The student will co-design a decision-support dashboard to make results accessible to policymakers, land managers, and conservation practitioners.
Training and skills development: The candidate will gain advanced training in AI and machine learning, bioinformatics, ecological modelling, and science-policy communication. Anticipated outcomes include a transferable AI framework for biodiversity forecasting, a novel digital twin, and a practical tool for guiding nature-positive decisions. This project offers an an exceptional opportunity to contribute to urgent global challenges while building a future-facing skill set at the interface of bioscience and AI. You will be supervised by Prof. Luisa Orsini, Dr. Jiarui Zhou, and Dr. Louis Dawson. As a BISCA PhD student, you will join a vibrant, interdisciplinary research network. You will be equipped with transdisciplinary expertise, fostering your growth as a future leader in sustainability and climate action, capable of driving meaningful change.
How to apply:
For more information and to apply for a studentship, please visit: https://www.birmingham.ac.uk/research/centres-institutes/birmingham-institute-for-sustainability-and-climate-action-bisca/phd-studentships
Funding Notes
These BISCA PhD Studentships provide coverage for Home tuition fees and a UKRI-rate stipend (£20,780 for 2025/26), alongside access to additional funding for training and development opportunities.
International applicants are welcome. However, international candidates will need to secure additional funding to cover the difference between Home and International tuition fees, and potentially other living costs. We strongly advise discussing your full funding requirements with your proposed supervisor as part of your application preparation.
