University of Leeds — Leeds University Business School
Supervisors: Dr Simon van Baal, Dr Sajid Siraj
Application deadline: Wednesday, January 13, 2027
Competition Funded PhD Project (Students Worldwide)
About the Project
Background
Climate change and biodiversity loss create positive feedback loops: the climate changes, more habitats are disrupted and carbon absorption is reduced. Around the world, countries use different methods to decide which habitats to protect. However, these decisions have no shared, structured, data-driven approach. An adaptable framework informed by literature on decisions under uncertainty, data science, and climate science would help identify which habitats are most important to protect. This…
project will explore three key areas: (a) the range of factors relevant to conservation (e.g., ecological value, climate impact, and social importance), (b) the views and priorities of people involved, and (c) how to assess each habitat on these factors with appropriate margins for uncertainty. The project aims to mitigate biodiversity loss and climate change. Our adaptive methodological framework will guide the development of a tool to support conservation-related policies.
PhD Project
Ecosystems are complex, and deciding which habitats to save can have outsized effects on our planet. Providing an adaptive framework can guide conservation policy. We list the research questions and aims below:
A. The prioritisation problem
- What is the structure of criteria for a conservation priority decision, and what are the preferences of the various stakeholders across them?
- How should each habitat be evaluated on these criteria (ecological value, climate impact, cost, interdependency with other habitats), and which habitats are disproportionately affected by harsher IPCC AR6 climate scenarios?
- Combining this information via MCDA, what ranking of priorities results, and how does it correspond to guidelines from (inter)national conservation organisations?
B. Handling uncertainty
- What are the dominant sources of uncertainty in climate projections, ecological data, and stakeholder preferences, and how large is each relative to the others?
- How can simulation-based, stochastic MCDA methods rank habitats by producing rank-robustness measures rather than a single deterministic ranking, and which habitats’ priority is robust versus sensitive to that uncertainty?
- What is the most effective way, including simulation-based interactive visualisation, to communicate the ranking and its uncertainty so policymakers can judge how much to trust it?
Candidate Outcomes:
You will develop your data collection skills using surveys, interviews, and accessing secondary data. You will learn to apply MCDA to problems under multidimensional uncertainty, likely producing a novel approach. You will expand your network among activists, experts and policymakers. You will also gain topic expertise in decision science, climate science and conservation.
Research Outcomes:
– Open-source database with the relevant criteria for conservation priorities and anonymised preferences of stakeholders.
– An interactive data-driven decision support tool for policymakers.
Applicant Profile
We, as a research team, acknowledge there are systemic inequities in higher education and research, especially at the doctoral level. Furthermore, especially when it comes to biodiversity loss and climate change, the research is usually conducted by people distant from those who are impacted most heavily, primarily in the Global South. We would encourage international students from LMIC countries to apply for this research project.
What do you bring:
Required
– Previous exposure to developing your academic writing.
– You have a strong interest in conservation.
– You have a background in ecology, conservation, or climate science.
Desired
– You are confident in managing data and have programming skills (preferably R or Python).
Funding Notes
This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a ‘Flexible Fund’ for special projects.
International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS)
Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.
More information is available on the UNRISK website: View Website
References
A good starting point for background reading on the topic:
A UN blog on biodiversity and climate change: https://www.un.org/en/climatechange/science/climate-issues/biodiversity
Previous major advances in the field:
– https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001991
– https://www.cpsg.org/sites/cbsg.org/files/documents/method for the assessment of priorities_0.pdf
Uncertainty in MCDA:
– https://link.springer.com/chapter/10.1007/978-3-319-47540-0_5
An inspiration for how we could think of solving this problem:
– https://github.com/sjharw/Biobank-Prioritisation-Tool
Project supervisors
Dr Simon van Baal
Dr Simon van Baal’s profile is coming soon.
Dr Sajid Siraj
Dr Sajid Siraj holds a PhD in Computer Science from the University of Manchester, United Kingdom. He also earned an MSc in Embedded Systems from the same institution and a B.E. in Electrical Engineering from the National University of Sciences and Technology in Pakistan. Dr Siraj is currently an Associate Professor at the University of Leeds, where he serves as the Program Director for the MSc Business Analytics & Decision Sciences and is the EDI Chair for the Data Scientists Development Programme. His research interests encompass decision support systems, explainable artificial intelligence, machine learning, data and text mining, and multi-criteria decision analysis. Dr Siraj has engaged in various projects involving text analytics, dominance-based rough sets analysis, imbalanced classification, and visual aids, applying his expertise to fields such as seismic data processing and telecommunications. He is a member of several professional organisations, including the Chartered Management Business Educator (CMBE), Fellow of the Higher Education Academy (FHEA), and the International Society on MCDM.
Research interests
Dr Sajid Siraj’s research interests encompass data mining, machine learning, and decision support systems. Recent projects have involved text analytics, dominance-based rough sets analysis, imbalanced classification, and the use of visual aids. Dr Siraj is also focused on the theory and applications of multi-criteria decision analysis, particularly in contexts where decision-makers face challenges in optimising or satisfying multiple, often conflicting, objectives. His expertise extends to machine learning, data wrangling, agent-based simulation, and software development, which he has applied in various domains including seismic data processing, telecommunication records processing, call queue management, and the development of decision support systems.