Senior Research Associate in Computer Vision and 3D Reconstruction for Wildlife Conservation

Manchester Metropolitan University is one of the UK's largest universities, with a strong commitment to interdisciplinary research addressing real-world challenges. Our newly established School of Biological and Chemical Sciences, within the Faculty of Science and Engineering, brings together expertise spanning biodiversity, zoology, ecology, physiology and the biomedical sciences. The University has growing strengths in artificial intelligence, data science and computational research, supported by its new Centre for Digital Data Research (CDDR).

You will join the School's Biodiversity and Environmental Resilience research group, where researchers investigate animal biology, evolution, behaviour and conservation, and develop innovative approaches to understanding and protecting the natural world. Our research combines traditional biological expertise with emerging technologies, including artificial intelligence, advanced 3D imaging and computational modelling. You will benefit from access to specialist research facilities, including imaging equipment in the University's new £1m Centre for Digital Modelling and Analysis for Cultural Heritage (D-MACH), and opportunities to engage with the wider Species Biology and Conservation (SBAC) research community.

This position is funded by the Natural Environment Research Council (NERC) through Phase 1 of its Artificial Intelligence for Environmental Science (AI4ES) programme. The project, Using Camera Trap Images to Reconstruct Wildlife 3D Body Size and Condition, aims to develop innovative AI-based methods for measuring animal size, shape and body condition from standard camera-trap photographs.

By combining monocular depth estimation with single-image 3D reconstruction, the project will develop new approaches to recovering quantitative, real-world measurements of wild animals without the need for physical handling. Working with Chester Zoo and the Max Planck Institute for Evolutionary Anthropology, methods will be developed and validated using advanced 3D imaging techniques, including LiDAR and photogrammetry, with the longer-term aim of supporting non-invasive wildlife health monitoring and conservation.

The project is led by Dr Charlotte Brassey (Principal Investigator), alongside Manchester Met colleagues Dr Selvino de Kort and Dr Bradley Cain, and external collaborators Dr Peng Wang (University of Surrey), Dr Lisa Holmes (Chester Zoo) and Dr Adam van Casteren (Max Planck Institute for Evolutionary Anthropology).

Key Responsibilities:

- Benchmark and fine-tune state-of-the-art monocular depth-estimation models using camera-trap imagery.
- Develop and evaluate AI-based methods for single-image 3D reconstruction of animals, integrating metric depth information to recover body dimensions and shape in real-world units.
- Curate and manage colour, LiDAR, photogrammetry and 3D datasets.
- Develop robust, reproducible computational pipelines using Python and relevant deep-learning frameworks.
- Undertake quantitative validation, statistical analysis and evaluation of model performance, accuracy and uncertainty.
- Collaborate with project partners to undertake data collection and validation at Chester Zoo.
- Contribute to the development and release of open-source software, datasets and trained models.
- Prepare research findings for publication in peer-reviewed journals and presentation at national and international conferences.
- Contribute to project management, dissemination, stakeholder engagement and the development of follow-on funding applications, including Phase 2 of the NERC AI4ES programme.

Essential Criteria:

- A PhD (or close to completion) in a relevant discipline, such as Computer Science, Artificial Intelligence, Machine Learning, Robotics, Computational Biology, Neuroscience, Engineering or another quantitative or computational discipline.
- Strong programming skills in Python.
- Practical experience with deep-learning frameworks such as PyTorch, TensorFlow or equivalent.
- Research experience developing, adapting and/or evaluating computer-vision or machine-learning models.
- Experience of quantitative analysis and rigorous evaluation of computational methods.
- Evidence of making a demonstrable, independent contribution to research projects.
- Excellent written and verbal communication skills, with the ability to work effectively within an interdisciplinary research team.
- Excellent organisational skills, attention to detail and the ability to manage research activities and deadlines.
- Ability to travel to Chester Zoo and undertake on-site data collection.

Benefits:

- 35 days leave plus Christmas closure and bank holidays
- Travel loan
- Wellbeing platform
- Life insurance
- Access to campus facilities
- Enhanced parental leave
- High-quality guaranteed pension

Manchester Metropolitan University fosters an inclusive culture of belonging that promotes equity and celebrates diversity. The University supports a range of flexible working arrangements, including hybrid and tailored schedules. Manchester Met is a Disability Confident Leader.