We use some essential cookies to make this website work.
We’d like to set additional cookies to understand how you use forestresearch.gov.uk, remember your settings and improve our services.
We also use cookies set by other sites to help us deliver content from their services.
PhD, MSc, BSc.
Tim works in the Soil Sustainability Research Team and alongside the Entomology Team in developing novel methodologies for automating biodiversity identification based on machine learning (ML) and contributing to research projects related to forest soil invertebrate ecology and identifying forest pests. His work encompasses the entire workflow of building complete end-to-end ML systems: from hardware setup and engineering control software for data capture; to dataset creation and curation; to building and training models; and finally for deployment of those models, including on embedded and constrained devices, to perform inference and classification. He also acts as an advisor to other groups on the application of AI in ecology and forestry.
Before joining FR in early 2024, Tim worked in several technology start-up businesses as a developer, manager, and technical director. Prior to that he worked on a wide range of academic projects ranging from automated animal behaviour tracking to construction site progress monitoring and skin cancer classification. He has a PhD in Computer Vision and an MSc in AI (Robotics) from Edinburgh University, alongside a BSc in Computer Science & AI from the University of Aberdeen.
Member of the British Machine Vision Association
Member of the British Ecological Society
Forest Research
Northern Research Station
Roslin
Midlothian
EH25 9SY
Cookies are files saved on your phone, tablet or computer when you visit a website.
We use cookies to store information about how you use the dwi.gov.uk website, such as the pages you visit.
Find out more about cookies on forestresearch.gov.uk
We use 3 types of cookie. You can choose which cookies you're happy for us to use.
These essential cookies do things like remember your progress through a form. They always need to be on.
We use Google Analytics to measure how you use the website so we can improve it based on user needs. Google Analytics sets cookies that store anonymised information about: how you got to the site the pages you visit on forestresearch.gov.uk and how long you spend on each page what you click on while you're visiting the site
Some forestresearch.gov.uk pages may contain content from other sites, like YouTube or Flickr, which may set their own cookies. These sites are sometimes called ‘third party’ services. This tells us how many people are seeing the content and whether it’s useful.