PhD Studentship in Machine Learning to Predict Nitrogen Leaching at Field Level

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 1 October 2024 or later. You can submit your application via the link under 'how to apply'.

Title

PhD position in machine learning to predict nitrogen leaching at field level

Research area and project description

Nitrate leaching from farmers’ fields is a major pollutant of fresh waters, and farmers are encouraged to use management practices to reduce nitrate leaching. However, existing nitrate-leaching models are not accurate enough, don't include actionable management variables, and don’t provide predictions at field-level. This hampers adopting farming practices to reduce leaching.

The PhD position is part of the NITAGRO project that aims to deliver more accurate, field-level predictions of nitrate leaching. The project will use data-driven approaches and machine learning methods to integrate multiple data sources with different granularities ranging from monitoring networks, field samples, weather data, crop information, remote sensing, and historical data. The PhD will be part of a team that will focus on the development of a new prediction model and that may need to address spatiotemporal modelling, use of satellite images, methods for integration of heterogeneous data, and evaluation of uncertainties by Bayesian methods. The PhD will need to collaborate with project partners from Aarhus University (departments of Ecoscience and Agroecology) and SEGES. Measured nitrate and leaching data will be available and these data will be used to evaluate prototype models for practical utility.

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.

Qualifications and specific competences

Applicants for the PhD position should (expect to) have an MSc degree in a computational statistical field (biostatistics, applied statistics, data science, computer science, computational biology) or in agronomy or ecology with a statistical and computational background.

The ideal candidate has a good theoretical background in machine learning and statistics with adequate computing experience (e.g., R, Julia, Python) and experience using high-dimensional data in predictive modelling. Prior knowledge of the application field is not required, but an interest in sustainable agriculture and environment will be valued. The role requires proficiency in English, both written and verbal, good writing and presentation abilities, the capability to collaborate well with the research team while efficiently handling responsibilities, and a mindset to generate own ideas and take ownership of the PhD research project.

The application material should include a small research plan with proposed research ideas (10-20 lines), either as part of the cover letter or as separate document. This research proposal can include the candidate’s ideas on integrating data, use of prior knowledge, statistical algorithms for predictive modelling, and any personal interests in these areas.

Place of employment and place of work

The place of employment is Aarhus University, and the place of work is Center for Quantitative Genetics and Genomics, Aarhus University, C. F. Møllers Alle 3, 8000 Aarhus C, Denmark

Contacts

Applicants seeking further information are invited to contact:

• Luc Janss, luc.janss@qgg.au.dk (main supervisor)
• Franca Giannini-Kurina, francagk@agro.au.dk (co-supervisor)

How to apply

Please click on the ‘Apply’ button above to read the full job description and submit your application.

Application deadline is 1 August 2024 at 23:59 CEST.

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