Postdoc: Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing

The Department of geosciences and natural resource management offers a 19-month Postdoctoral position in the field of Deep Learning and Remote Sensing, with a specific focus on developing, training and application of deep learning models from drones and satellite remote sensing data to identify urban risk areas for mosquito-borne diseases in East African cities.

This position is part of an inter-disciplinary project called Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing and involves close collaboration with Department of Computer Science, University of Copenhagen. This project will apply deep learning in the research domains of epidemiology, architecture, and remote sensing on multi-scale data, spanning from the level of the individual household to the metropolitan level to identify risk areas of mosquito-borne diseases in East African cities. While the links between housing conditions and mosquito-borne diseases are increasingly recognized, the relation between attributes of urban environments and vector-borne diseases remains understudied, decreasing the efficacy of measures to address health issues in African cities.

The postdoc’s duties will include: Developing macro scale data acquisition and analysis, including purchasing the remote sensing data (from fixed-wing UAVs and satellite remote sensing), linking the detailed labelled geospatial surveys of project partners (household surveys and attributes of the urban environment) to large scale remote sensing datasets as well as developing, training and application of deep learning models to identify and predict risk areas for mosquito-borne diseases. As building typology, roof material, and urban density measures are expected to be of importance, the work will require methodological research in deep learning. The semantic segmentation will not only need to distinguish various housing types but also take long-range spatial information into account (e.g., water bodies, vegetation, and population density). Accordingly, different methods to derive the risk factors from the semantic segmentations will be applied, either in a two-step process or an end-to-end approach.

Eligible candidates:

• The applicants should hold a PhD degree in remote sensing, geoinformatics, computer vision, AI or related fields.
• Candidates with a solid understanding of satellite images, spatial analyses, machine learning and deep learning.
• Candidates with strong programming skills as well as having proven experience with handling, processing and analyzing large image datasets.
• Experienced in multidisciplinary team-based activities with the ability to effectively communicate with colleagues and with staff from the partners of a project.
• Proficiency in spoken/written English, including skills in academic writing and publishing in peer-reviewed journals are essential. It is considered an advantage if candidates have proven records of innovative remote sensing-based work.

The position will be an integral part of a 5-year project financed by the Novo Nordic Foundation. The project and the position will be a close collaboration between the Royal Danish Academy (PI), The London School of Hygiene & Tropical Medicine, Ifakara Health Institute, Tanzania and University of Copenhagen (Department of Computer Science & Department of Geosciences and Natural Resource Management). The Postdoc will is expected to work closely together with other team members of the project.

Work environment

Your workplace will be the Department of Geosciences and Natural Resource Management (IGN), but also partly at the Department of Computer Science (DIKU). IGN conducts research and education on the past, present and future physical, chemical and biological environments of the Earth and their interactions with societal and human systems to provide graduates and research in support of sustainable future solutions for society. The department has strong experience in interdisciplinary collaboration within and beyond the department.

Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty/organisation/. Inquiries about the position can be made to Professor Rasmus Fensholt rf@ign.ku.dk.

The position is open from June 1st 2026.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Terms of employment

The position is covered by the Memorandum on Job Structure for Academic Staff.

Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below

Please include

• Curriculum vitae
• Diplomas (Master and PhD degree or equivalent)
• Research plan – description of current and future research plans
• Complete publication list
• Separate reprints of 3 particularly relevant papers

The deadline for applications is 28th of February 2026, 23:59 GMT +1.

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Interviews will be held during last part of March 2026.