The University of Copenhagen is offering a 19-month postdoctoral position focused on the risk assessment of vector-borne diseases using deep learning and remote sensing. The successful candidate will join the Department of Geosciences and Natural Resource Management (IGN) and collaborate closely with the Department of Computer Science (DIKU) and international partners, including the Royal Danish Academy, the London School of Hygiene & Tropical Medicine, and the Ifakara Health Institute in Tanzania. This interdisciplinary project, funded by the Novo Nordic Foundation, aims to develop, train, and apply deep learning models to drone and satellite remote sensing data to identify urban risk areas for mosquito-borne diseases in East African cities.

The research will involve macro-scale data acquisition and analysis, linking detailed geospatial surveys with large-scale remote sensing datasets, and developing advanced deep learning models for semantic segmentation of urban environments. The project will address the understudied relationship between urban attributes and vector-borne disease risk, with a focus on building typology, roof material, urban density, water bodies, vegetation, and population density. The postdoc will contribute to methodological research in deep learning, applying both two-step and end-to-end approaches to derive risk factors from spatial data.

Applicants must have a PhD in remote sensing, geoinformatics, computer vision, AI, or related fields, with strong programming skills and experience in handling large image datasets. Proficiency in English and a track record of academic publishing are required. The position offers a collaborative and diverse work environment, with terms of employment governed by Danish academic staff agreements. The application deadline is 28 February 2026, and the position starts on 1 June 2026. For more information, contact Professor Rasmus Fensholt at [email protected].

To apply, submit your application electronically via the University of Copenhagen job portal, including your CV, diplomas, research plan, publication list, and three relevant papers. For further details, visit the official job posting and department websites.