Post-Doctoral Research Associate in Computer Vision/Machine Learning

University of Lincoln
April 08, 2024
Offerd Salary:£37,099
Working address:N/A
Contract Type:fixed term until 28
Working Time:Full time
Working type:N/A
Ref info:N/A
Post-Doctoral Research Associate in Computer Vision/Machine Learning School of Computer Science

Location: Lincoln Salary: From £37,099 per annum Please note, this post is fixed term until 28 February 2027, and full time at 1.0 FTE. Closing Date: Monday 08 April 2024 Interview Date: Wednesday 24 April 2024 Reference: CHS104

We are looking for a passionate and motivated Post-Doctoral Research Associate (PDRA) to join an interdisciplinary team of researchers on a 3-year project “Using artificial intelligence for automated habitat assessment ( AI-Hab )” funded by the Natural Environment Research Council (NERC).

Using tens of thousands of images from the long-term, nationally representative Countryside Survey, AI-Hab will leverage the latest advances in computer vision and deep learning to deliver an automated monitoring tool for habitat classification. Habitat recognition is essential to the delivery of Biodiversity Net Gain (BNG), which seeks to ensure that land development has a measurably positive (+10%) impact on biodiversity. Led by the University of Lincoln, this project is a collaboration between the School of Life and Environmental Sciences (Dr Lan Qie, project lead), the School of Computer Science (Dr James Brown, Dr Petra Bosilj), and the UK Centre for Ecology and Hydrology (UKCEH). The resulting tool will be integrated into the E-Surveyor application developed by UKCEH.

As part of this exciting opportunity, you will be responsible for the development, validation, and deployment of a deep learning model for habitat classification. You will need to have experience in building deep learning workflows using established tools (e.g., PyTorch), proficiency in Python or another suitable programming language (e.g., C++), and a passion for interdisciplinary research in support of environmental conservation. You should also have, or be close to completing, a PhD in computer science or a related field. There will be opportunities for you to develop your own research interests, attend high-profile international conferences, and publish your work in both computational and ecological venues.

If you would like to have an informal discussion about the role, please contact either Dr James Brown (line manager, he/him), at [email protected], Dr Petra Bosilj (project co-lead, she/her), at [email protected], or Dr Lan Qie (project lead, she/her), at [email protected].

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