Utilizing multisource data to optimize nitrogen fertilizer application strategy in agricultural crop production - ref. g26116803
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 2025 or later. You can submit your application via the link under 'how to apply'.
Title: Utilizing multisource data to optimize nitrogen fertilizer application strategy in agricultural crop production
Research area and project description:Department of Agroecology at Aarhus University, Denmark, is offering a PhD position in utilizing multisource data to optimize nitrogen fertilizer application strategy in agricultural crop production. The position is available for three years.
The candidate will work with modelling based on large quantities of spatiotemporal data from farmer's fields and field plot experiments. The input data from satellites, weather observations, soil samples, and field management data is massive, and it is expected that deep learning techniques like Convolutional Neural Networks, Transformers, and LSTM should be used to extract spatiotemporal patterns and correlations in the input data.The project has access to highly accurate explainable input field data, and the goal is to explain the response variables like crop yield, crop quality, and soil quality, based on the explainable input data. An interesting research problem here is the imbalance between the amount of input data, and the available labels, which will probably require using semi-supervised learning techniques.
The successful candidate will work on an ongoing research project where the aim is to improve crop production (with a special focus on Danish agricultural systems) and lower negative environmental- and climatic impacts. The results will be incorporated into an existing decision-support system, and it is, therefore, important that you are willing to collaborate with both companies, farmers, and farmers organizations.Being able to bring these pieces of information together, ensure implementation together with software colleagues and deliver real-time solutions for nutrient application rates and forms is a key focus area for Danish farming and consequently for the Danish society.
Project description
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Qualifications and specific competences:A MSc relevant to modelling in Agricultural Science, Physics, Ecology, Biology, Environmental Science, or related fields.
Experience and skills in programming languages (e.g. Python/R) and analytical skills in model evaluation (strongly preferred), as well as experience with multisource data and spatial data analysis including remote-sensing satellite data and yield data handling, is a strong advantage for the position.
- An interest in data assimilation
- Basic knowledge in remote sensing
- An ability and interest to collaborate across disciplinary boundaries
- An interest in sustainable agriculture and motivation to contribute to green transition
- An ability to communicate effectively in English, and will contribute to the dissemination, teaching, and reporting of research results in high-impact scientific journals
- Good scientific and technical writing skills
- 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 Aarhus University, Department of Agroecology, 4200 Slagelse
Contacts:Applicants seeking further information are invited to contact:
- René Gislum, rg@agro.au.dk (main supervisor)
How to apply: Please follow this link to submit your application.
Application deadline is 15 December 2024 at 23:59 CET.
Preferred starting date is 15 February 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.
All interested candidates are encouraged to apply, regardless of their personal background.
Please note in your application that you found the job at Jobindex