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 01 July 2025 or later. You can submit your application via the link under 'how to apply'.
Title
Remote Sensing for Climate-smart Agriculture
Research area and project description
Department of Agroecology at Aarhus University, Denmark, is offering a PhD position in remote sensing for climate-smart agriculture, starting 01 July 2025 or earliest possible thereafter. The position is available for a 3-year period.
Food security, climate change and loss of biodiversity represent three of today’s major environmental sustainability challenges. Finding solutions to these challenges requires studies that extend across multiple spatial and temporal scales. The Pioneer Center Land-CRAFT was established in June 2022 to undertake fundamental and applied research that addresses these issues. The center brings together experts on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University of Copenhagen, as well as international partners. Field experiments, digital technologies -- including modelling and remote sensing, as well as interactions with stakeholders are key components of Land-CRAFT.
Climate-smart agriculture aims to provide innovative solutions to improve crop production, reduce greenhouse gas emissions, and strengthen agroecosystem resilience to climate extremes. Timely information of agroecosystem dynamics is highly important for understanding the complex interactions among crops, management activities and environmental factors. This project aims to develop novel remote sensing algorithms to monitor crop nitrogen and agroecosystem productivity for climate-smart agriculture. The remote sensing derived information will be used to inform climate-smart agriculture practices and policies for the Danish and EU wheat cropping systems. In this project, the PhD student will work on the following tasks.
(1) To develop novel remote sensing algorithms to integrate soil-vegetation radiative transfer models and deep learning to quantify crop nitrogen and productivity from satellite remote sensing at the regional scale.
(2) To assess the potential impacts of climate change and management practices on crop nitrogen, crop productivity, and nitrogen use efficiency.
(3) To conduct data-driven analysis to identify optimal nitrogen fertilizer rates to maximimize crop productivity while minimizing negative negative impacts (greenhouse gas emissions and nutrient leaching).
(4) To collaborate with stakeholders, including farmers, policymakers, and researchers for field data collection and research dissemination.
This Ph.D. position offers an opportunity to collaborate inter-disciplinary with other scientists on cutting-edge research in remote sensing for climate-smart agriculture to enhance agroecosystem productivity and reduce greenhouse gas emissions.
- Project description. For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.
We are searching for a highly motivated candidate who has an M.Sc. degree in agriculture, environmental sciences, data sciences, geography, ecology, or closely related fields.
Demonstrated oral and written communication skills in English are required. The applicant must be able to present results at international conferences and publish in international peer-reviewed journals. The applicant should be enthusiastic about working in an international and interdisciplinary academic environment. Applications from women and people from underrepresented groups are strongly encouraged. Further, we will prefer candidates with some of the following qualifications:
- Good scientific and technical writing skills
- Rich experience in satellite remote sensing for crop nitrogen and yield predictions
- Strong skills in programming (preferably in Python), large-scale remote sensing data processing, radiative transfer modelling and deep learning
- An ability to communicate effectively in English, and will contribute to dissemination, teaching and reporting research outcome in high-impact scientific journals
- An ability and interest to collaborate across disciplinary
- Collaborative and interpersonal skills, and will contribute positively to the social working environment
The place of employment is Aarhus University, and the place of work is Ole Worms Allé 3, Building 1171, 8000 Aarhus, Denmark.
Contacts
Applicants seeking further information are invited to contact:
- Professor Klaus Butterbach-Bahl, klaus.butterbach-bahl@agro.au.dk (main supervisor)
- Tenure-track assistant professor Sheng Wang, swan@agro.au.dk (co-supervisor)
- Professor Claire Treat, claire.treat@agro.au.dk (co-supervisor)
Please follow this link to submit your application.
Application deadline is 1 March 2025 at 23:59 CET.
Preferred starting date is 1 July 2025.
For information about application requirements and mandatory attachments, please see our application guide.
Please note:
- Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
- 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.
INFORMATIONER OM STILLINGEN:
- Arbejdspladsen ligger i:
Aarhus Kommune
-Virksomheden tilbyder:
-Arbejdsgiver:
Aarhus Universitet, Ole Worms Allé, 8000 Aarhus C
-Ansøgning:
Ansøgningsfrist: 01-03-2025;
Se mere her: https://job.jobnet.dk/CV/FindWork/Details/951032b4-790d-42f1-8e07-5a72dd6e9ab4
