Linking modelling, precision agriculture and remote sensing for spatial and temporal estimation of greenhouse gas emission and nitrogen leaching


BEMÆRK: Ansøgningsfristen er overskredet

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Agroecology programme. The position is available from 15 February 2024 or later. You can submit your application via the link under 'how to apply'.

Linking modelling, precision agriculture and remote sensing for spatial and temporal estimation of greenhouse gas emission and nitrogen leaching

Research area and project description:
The major challenge addressed is the systemic mismanagement of nitrogen (N) fertilizer in agricultural fields leading to problems such as leaching of nitrates into groundwater and emission of harmful greenhouse gases. Digital technologies are commercialized in agriculture (available from the early 1990s) but have failed with N fertilization. Despite agriculture is the least digitized sector (as highlighted at the last World Economic Forum) to make a reliable recommendation, researchers need to extract and simplify complex science into practical and relevant information enabling advisors and farmers to use such information to make N recommendations for individual fields.

But severe bottlenecks in research, development and application of precision agriculture cause key research to be disconnected. With this project we propose to develop a framework that explores and fills the gaps within the system. We focus on ways to a strategic and tactical N management that maximizes farmers’ income and minimize N leaching and greenhouse emissions.

Through this project the candidate will help developing a N management system that minimizes N losses in arable farming. This is achieved by combining sensing of soil and crop at high spatial and temporal resolution with crop-soil models; observations and modeling are combined using a digital twin (DT) approach. A DT provides not only a real-time, up-to-date view of the state of crop and soil, but also allows to make predictions about future states of the system under different management options.

Project description (½-4 pages): This document should describe your ideas and research plans for this specific project. If you wish to, you can indicate an URL where further information can be found.

Qualifications and specific competences:
The expectations are that the candidates holds a degree in Agricultural science or related discipline, has experience with programming and has basic understanding of system-based modelling and agriculture.

Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is Blichers Alle’ 20, Tjele, 8830, Denmark.

Applicants seeking further information are invited to contact:

How to apply:
Please follow this link to submit your application. Application deadline is 12 December 2023 at 23:59 CET. Preferred starting date is 15 February 2024.

For information about application requirements and mandatory attachments, please see our application guide.

Please note:
  • The programme committee may request further information or invite the applicant to attend an interview.
  • Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.



- Arbejdspladsen ligger i:

Viborg Kommune

-Virksomheden tilbyder:


Aarhus Universitet, Blichers Alle, 8830 Tjele


Ansøgningsfrist: 12-12-2023; - ansøgningsfristen er overskredet

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Denne artikel er skrevet af Emilie Bjergegaard og data er automatisk hentet fra eksterne kilder, herunder JobNet.
Kilde: JobNet