Applying computer vision to natural history collections for ecological, taxonomic, and conservation research

Recent mass digitization efforts of Natural History Collections (NHCs), pioneered by institutions like the NHM, have given scientists across the globe access to untold numbers of specimens and associated meta-data (e.g. date and location of collection) for research. Using these digitized collections, the NHM and colleagues are developing new computer vision techniques to automatically extract biological and historical information from NHCs across the tree of life and from a wide array of habitats. Crucially however, these massive datasets must now be focused towards unlocking their potential for answering ecological, taxonomic, and conservation questions. This studentship will explore the vast amount of computer vision data extracted from NHCs and apply them to the following questions:
Can computer vision be used to fill taxonomic gaps and facilitate new species descriptions?
Can the morphological features (e.g. body size, shape, colour) extracted from computer vision be used to examine biotic response to climate change (e.g. temperature-size responses)?
Can computer vision be used as a means for rapid biodiversity assessments of field collected invertebrates from a wide range of habitats (e.g. forests to the deep sea)?
This PhD will be among the first to apply computer vision methods to taxonomic, ecological, and conservation questions.