Project Overview: Biodiversity loss is a critical global challenge, driven by climate change, land-use transformation, and human disturbance. Predicting how ecological communities respond to these pressures is a complex problem due to the dynamic and nonlinear interactions among species and their environments. This PhD project, hosted at University College London, aims to revolutionize biodiversity forecasting by integrating artificial intelligence (AI) and network science with traditional ecosystem modelling. Research Focus: The project will develop AI-integrated network ecosystem models that learn, represent, and forecast biodiversity change. Biodiversity dynamics are conceptualized as a spatiotemporal network problem, where species, their interactions, and environmental drivers form a dynamic graph evolving under anthropogenic pressure. The student will utilize large-scale biodiversity sensor networks (camera-trap and acoustic arrays), Earth observation, and environmental data to construct multi-layer species interaction networks, capturing community structure and change over time. Methodology: The research framework consists of three layers: (1) Data layer—harmonizing heterogeneous ecological data into a spatiotemporal event graph; (2) Interaction layer—applying graph neural networks (GNNs) and probabilistic modelling to infer dynamic species–species and species–environment links, regularized by ecological priors; (3) Process layer—integrating learned networks into mechanistic ecosystem models to simulate dispersal, resource use, and competition, producing calibrated forecasts of biodiversity change under various scenarios. Case Studies & Impact: The project will leverage long-term datasets from ecosystems under strong human pressure, such as the Maasai Mara (Kenya) and the Terai Arc (Nepal). These sites provide rich data for quantifying network reconfiguration, predicting biodiversity outcomes under land-use or climate scenarios, and assessing conservation and ecosystem resilience implications. The research sits at the intersection of ecology, AI, and environmental data science, offering transformative advances in predictive biodiversity science and actionable conservation forecasts. Training & Opportunities: The student will receive comprehensive training in applied AI, biodiversity, and transferable professional and research skills. The project includes a placement with an AI-INTERVENE partner (3–18 months) and opportunities to present at national and international conferences, positioning the student at the forefront of the discipline and enhancing future employment prospects. Eligibility: Suitable candidates will have a degree in ecology, engineering, environmental, physical, or computer science, with experience in computing and mathematics. The interdisciplinary supervisory team will support the student in developing expertise in ecological theory, computational modelling, and geospatial data analysis. Funding: The studentship is fully funded by the AI-INTERVENE NERC Doctoral Focal Award, subject to a competitive selection process. Application Deadline: January 19, 2026. References: Emergent Global Patterns of Ecosystem Structure and Function from a Mechanistic General Ecosystem Model ; Heterogeneous graph neural networks for species distribution modelling . How to Apply: Apply via the University College London or University of Reading portal as indicated in the project listing. Prepare a CV and cover letter detailing relevant experience. For further information, contact the supervisory team.

Project Overview: Biodiversity loss is a critical global challenge, and effective ecosystem protection requires accurate, continuous, and large-scale data collection. However, many natural habitats are remote, fragile, or hazardous, making traditional fieldwork difficult. This PhD project, hosted at University College London, aims to revolutionize biodiversity monitoring by developing animal-inspired cognitive navigation for autonomous legged robots. These robots will be designed to safely and intelligently traverse wild environments, collecting ecological data with minimal disturbance to wildlife and habitats. Research Focus: The student will investigate how artificial intelligence and robotics can learn from nature, drawing inspiration from the remarkable navigation abilities of animals such as insects, mammals, and birds. The project will integrate biological strategies into robotic systems, enabling robots to perceive, plan, and adapt their movements in complex terrains like forests, grasslands, and wetlands. The goal is to create AI-driven legged robots capable of autonomous, non-invasive biodiversity monitoring, complementing traditional ecological surveys and reducing the environmental footprint of data collection. Interdisciplinary Training: The successful candidate will receive comprehensive training in robot perception, cognitive AI, and ecological applications. The project is supported by supervisors from UCL’s Intelligent Robotics Group and the Centre for Biodiversity and Environment Research, with opportunities for collaboration with leading organizations such as the Zoological Society of London and the UK Centre for Ecology & Hydrology. The training program includes a placement with an AI-INTERVENE project partner (3-18 months), as well as opportunities to present research at national and international conferences. Eligibility: This position is suitable for students with a background in robotics, computer science, artificial intelligence, engineering, or physical sciences. Candidates with experience or strong interest in machine learning, computer vision, or autonomous systems are encouraged to apply, as are those from environmental or biological sciences with computational or robotics experience. The project seeks enthusiastic individuals eager to apply AI to real-world biodiversity and ecological monitoring challenges. Funding: The studentship is fully funded by the AI-INTERVENE NERC Doctoral Focal Award, subject to a competitive selection process. This includes tuition fees and a stipend for living expenses. Application Process: Interested applicants should apply via the University College London application portal, referencing the AI-INTERVENE NERC Doctoral Focal Award. Prepare a CV and a statement of interest detailing relevant experience and motivation. Early contact with the supervisors is recommended for further information. The application deadline is January 19, 2026. Impact: By combining artificial intelligence with ecological expertise, this project aims to develop the next generation of environmentally integrated autonomous systems. The research will contribute to conservation robotics and sustainable exploration, enabling long-term monitoring and protection of ecosystems. The student will be at the forefront of interdisciplinary science, gaining skills and experience that open excellent future employment opportunities in academia, industry, and environmental organizations.

Project Overview: This PhD opportunity, hosted at University College London and administered by University of Reading's AI-INTERVENE department, tackles the urgent global challenge of biodiversity decline. The project leverages advanced computational approaches, including large language models (LLMs) and machine learning, to address critical data limitations in biodiversity assessment. By developing sophisticated extraction algorithms, the research aims to mobilize hidden biodiversity records from diverse sources such as non-English scientific literature, grey literature, and corporate documents. Research Focus: The project will create comprehensive temporal biodiversity baselines spanning 1800 to 2100, using multilingual LLM extraction algorithms to identify, validate, and contextualize biodiversity records across linguistic and geographical contexts. These data will support predictive models of wildlife abundance in response to land-use, climate, and management changes, utilizing gradient boosting techniques (e.g., LightGBM, XGBoost) to enhance model accuracy and identify species and regions in decline. Innovation & Impact: By unlocking previously inaccessible scientific information with AI tools and integrating these into predictive models, the research will generate unprecedented insights into long-term biodiversity trends. The project aims to reduce linguistic and geographical biases, contributing to a more equitable understanding of global biodiversity change. Outcomes will directly improve the Living Planet Index, a key global indicator for tracking wildlife abundance. Training & Development: The successful candidate will benefit from a comprehensive training programme in applied AI, biodiversity, and transferable research skills. A placement with an AI-INTERVENE project partner (3-18 months) is included, along with opportunities to present at national and international conferences, positioning the student at the forefront of the discipline and enhancing future career prospects. Funding: The studentship is fully funded by the AI-INTERVENE NERC Doctoral Focal Award, subject to a competitive selection process for the strongest applicants. Eligibility: Applicants should have strong computational skills, experience with machine learning, natural language processing, and programming in R or Python. Experience with large datasets and predictive statistical modelling is highly desirable. The project welcomes candidates from diverse academic backgrounds. Application Deadline: January 19, 2026. How to Apply: Apply via the FindAPhD project link. Prepare your application materials emphasizing relevant computational and data science experience. Selection is competitive; contact the department for further details if required. References: Key literature includes works by Cornford et al. (2021, 2023), Ledger et al. (2023), and the WWF Living Planet Report (2024).