Senior Research Associate In Computer Vision And 3D Reconstruction For Wildlife Conservation

Manchester Metropolitan University; Manchester; Presencial; GBP 38 000 - 48 000; Tempo integral

Resumo da oferta

Manchester Metropolitan University invites applications for a researcher to drive AI-based reconstruction of animal body size and shape from camera-trap imagery. You will develop, test and validate depth-estimation and 3D reconstruction methods, curate diverse datasets, and perform quantitative analyses to enable real-world measurements.

You will publish findings, contribute to Phase 2 funding, and engage with the SBAC seminar series and Biodiversity and Ecosystem Resilience community, including

Qualificações

• A PhD (or close to completion) in a relevant discipline (CS/AI/ML/Robotics/Computational Biology).
• Strong programming skills in Python.
• Experience with deep-learning frameworks (PyTorch, TensorFlow).
• Research experience in computer-vision or ML models.
• Experience with quantitative analysis and rigorous evaluation of computational methods.
• Evidence of independent research contribution.
• Excellent written and verbal communication within interdisciplinary teams.
• Strong organisational skills and ability to manage deadlines.

Responsabilidades

• Benchmark and fine-tune monocular depth-estimation models using camera-trap imagery.
• Develop AI-based methods for 3D reconstruction of animals with real-world units.
• Curate and manage colour, LiDAR, photogrammetry and 3D datasets.
• Develop reproducible computational pipelines using Python and DL frameworks.
• Perform quantitative validation and statistical evaluation of model performance.
• Collaborate with project partners for data collection and validation at Chester Zoo.
• Contribute to open-source software, datasets and trained models.
• Publish findings in peer-reviewed journals and present at conferences.
• Contribute to project management, dissemination and follow-on funding proposals (Phase 2).

Conhecimentos

Python programming

Deep learning

Computer vision

Data analysis

Research communication

Formação académica

PhD or near completion in Computer Science/AI/ML/Computational Biology

Ferramentas

PyTorch

TensorFlow

Descrição da oferta de emprego

About the Role

& Team

You will play a central role in the day-to-day technical development and delivery of the project, with responsibility for developing, testing and validating AI-based approaches to reconstructing animal body size and shape from camera-trap imagery. Your work will primarily involve computational research, including model development, dataset curation and quantitative analysis, alongside opportunities for practical data collection and validation at Chester Zoo.

You will be encouraged to take ownership of your research, contribute to peer-reviewed publications and conference presentations, and help shape future research directions, including the development of a larger Phase 2 funding application. You will also become part of the wider Biodiversity and Ecosystem Resilience research community, with opportunities to participate in the Species Biology and Conservation (SBAC) seminar series and professional development activities.

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.

Key Skills & Experiences

We are looking for a highly motivated researcher with strong computational skills and experience in computer vision, machine learning, AI-based image analysis and/or 3D reconstruction. We welcome applicants from a broad range of disciplinary backgrounds; previous experience in ecology or zoology is not required. This is an interdisciplinary project, and we recognise that relevant expertise can be developed through a variety of research backgrounds and career pathways. We therefore encourage applications from candidates who bring transferable skills and enthusiasm for the project, even if they do not meet every criterion listed below.

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.

To learn more about role and requirements, please read the Job Description provided below.