UNEP-WCMC is looking for a post-doctoral research scientist with a proven research record in the use of AI technologies to identify and aggregate novel sources of data. The successful candidate will use their technical expertise in LLMs and systems architecture to design and deliver a sampling framework that aggregates data within a multidimensional, geospatial data cube. The post-holder will sit within the Science Programme at UNEP-WCMC. However, they will be expected to collaborate closely with the Digital Transformation and Nature Conserved Programmes as well as with an external network of collaborators including Google and Cambridge University.
UNEP-WCMC attaches great importance to addressing safeguarding and ethical considerations in all activities carried out by its staff, including where partner organisations or individuals are part of the delivery of our work. This includes children and vulnerable adults in the community who may be vulnerable to abuse. UNEP-WCMC acts with integrity, is transparent and expects applicants to share the same values.
The post-holder will be part of an exciting new project that aims to use cutting edge techniques to assess the vulnerability of species to wildlife trade. They will form part of a team made up of data scientists, modelling scientists and experts in wildlife trade. To deliver novel sources of data, the successful candidate will work with the team to focus sampling efforts on data deficient species. They will also design LLM prompts to discover novel sources of data, to formulate data and implement validation of the data. The successful candidate will work closely with the modelling scientists to understand the structure of data required for their models. They will also design and populate a multidimensional, geospatial data cube that can be pipelined directly into models. The postholder may occasionally be required to help develop funding proposals related to novel uses of AI in the field of conservation and may also be called upon to represent UNEP-WCMC at external meetings, workshops and conferences.
The successful candidate will have a PhD in arelevant field or equivalent work experience. They will have expertise with LLMs to gather, structure or extract data. They will have experience handling large and complex datasets, especially those associated with cloud computing environments. It is not necessary for the post-holder to have experience working on wildlife trade or conservation-related topics. Indeed, any candidates who are interested in developing their data science skills to be used for the protection and restoration of nature are encouraged to apply.
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