Interpretable and Explainable Operations Research and AI-based Analytics for Systematic Conservation Planning in Antarctica

Effective conservation strategies are essential to safeguard Antarctica, its fragile ecosystem and unique biodiversity which faces growing threats from climate change and human activities. Working in the Securing Antarctica’s Environmental Future (SAEF) program, this ARC-funded, multi-disciplinary PhD project aims to review the literature and propose transparent and interpretable operations research and Artificial Intelligence techniques, with a focus on their application in systematic conservation planning

Systematic conservation planning (SCP) is an approach that systematically identify and prioritise areas for conservation efforts. The goal is to maximise the effectiveness of conservation actions while considering ecological, economic, social, and political factors. It aims to provide a structured and scientific foundation for making decisions about where to allocate limited resources for conservation. Various (meta)-heuristics and traditional optimisation and operations research methods have been proposed for systematic conservation planning.

While these techniques might provide mathematically ‘optimal’ or ‘near-optimal’ (in case of meta-heuristics) solutions, they often lack transparency and interpretability, making it difficult for decision-makers to understand why a particular solution was chosen and how it aligns with their goals. Furthermore, the application of modern algorithms that utilise parallel processing and accelerated computing, as well as their transparency, interpretability and explainability has not been thoroughly explored for conservation planning.

To this aim and guided by related research from the supervisory team, this project aims to explore and develop new efficient and  interpretable models, potentially utilising parallel and accelerated analytics, to address specific conservation challenges, including habitat preservation, species protection, and sustainable resource management. The project seeks to comprehensively review and synthesise current SCP approaches within an Antarctic context, highlighting the integration of both traditional and modern operational research and AI-based models.

It will evaluate the effectiveness of these newly proposed SCP strategies against the current state-of-the-art, analysing their transparency, interpretability, and explainability, and will suggest further explainable approaches where necessary. Additionally, the project intends to review and/or propose explainable AI frameworks for integrating these methods into SCP practices, aiming for wider applicability not only in Antarctica but also in other vulnerable ecosystems.

This project is part of the Securing Antarctica’s Environmental Future (SAEF) program, a collaborative partnership of international researchers and practitioners that will deliver research to forecast environmental change across the Antarctic region, to deploy effective environmental stewardship strategies in the face of this change, and to secure Antarctica as a natural reserve devoted to peace and science.

SAEF is an Australian Research Council (ARC) Strategic Research Initiative and acknowledges the significant investment from the ARC and contributing organisations.

Duration:

4 years (or full-time equivalent)

Application process:

Interested applicants need to prepare the following:

• a one-page cover letter outlining relevant experience
• a Curriculum Vitae (three-page max.)
• The most recent academic transcripts
• contact details for two academic referees

Applicant can begin in Autumn 2025

Eligibility requirements:

• Strong knowledge of optimisation methods, including linear programming, metaheuristics, and operations research.
• Familiarity with AI and machine learning techniques, particularly in explainability and interpretability.
• Proficiency in computer programming and relevant software tools for data analysis and modelling.
• Demonstrated ability to conduct independent research under guidance and contribute to academic publications.
• Interest in applying computational analytics approaches to real-world conservation challenges, particularly in vulnerable ecosystems such as Antarctica.
• A Masters or Class I Honours (or equivalent) undergraduate degree in a relevant field such as Environmental Science, Conservation Biology, Computer Science, or Operations Research.