Aarhus University, in collaboration with Nordic Beet Research (NBR), invites applications for an Industrial PhD fellowship focused on sustainable disease management in sugar beet. The position is offered through the Graduate School of Technical Sciences within the Agroecology programme and is expected to start on 1 May 2026 or shortly thereafter.
This PhD opportunity is ideal for candidates interested in plant pathology, integrated pest management (IPM), machine learning in agriculture, and industry–academia collaboration.
Key Facts at a Glance
- Application Deadline: 2 February 2026, 23:59 CET
- Preferred Start Date: 1 May 2026
- Programme: Agroecology (Industrial PhD)
- Employing Organization: Nordic Beet Research Foundation
- Academic Partner: Aarhus University
Research Background and Relevance
Sugar beet is a key crop for European agriculture, but its production is increasingly threatened by Cercospora Leaf Spot (CLS), caused by the fungal pathogen Cercospora beticola. CLS is globally recognized as the most yield-reducing foliar disease in sugar beet, and its incidence has increased markedly in Denmark over the past 5–6 years.
At the same time, European agriculture faces strong pressure to reduce pesticide use while maintaining high productivity. This creates an urgent need for data-driven, sustainable disease management strategies that balance crop protection, profitability, and environmental responsibility.
About the PhD Project: CercoCAST
This Industrial PhD is part of the CercoCAST project, which aims to develop and validate a novel Integrated Pest Management (IPM) decision-support system (DSS) for Danish sugar beet production.
The project integrates multiple data streams into one unified model:
- Weather-based disease forecasting
- Cultivar-specific resistance profiling
- Quantification of soil-borne pathogen inoculum
The overarching goals are to:
- Reduce fungicide use by at least 30%
- Increase sugar yield by approximately 10%
- Support Denmark’s green transition and the EU Farm-to-Fork strategy
Key Research Tasks and Responsibilities
The PhD candidate will work across both industry and academia, contributing to cutting-edge applied research with real-world impact.
Main responsibilities include:
- Developing weather-based disease forecasting models using epidemiology and machine learning
- Characterizing Cercospora resistance in sugar beet cultivars through field trials and greenhouse experiments
- Developing and validating molecular diagnostic methods, including droplet digital PCR (ddPCR), to quantify soil-borne inoculum
- Integrating all components into a digital IPM decision-support tool for growers
- Publishing results in high-impact scientific journals
- Presenting research at international conferences
- Participating in knowledge dissemination, including teaching, grower workshops, and industry meetings
Industrial PhD Structure
This is an Industrial PhD, meaning the candidate will be:
- Employed by Nordic Beet Research Foundation
- Enrolled at Aarhus University, Graduate School of Technical Sciences
The candidate is expected to split time equally between:
- Nordic Beet Research, Holeby, Denmark
- Aarhus University, Slagelse and related research facilities
Exact scheduling will be determined within the framework of the project.
About the Industry Partner: Nordic Beet Research (NBR)
Nordic Beet Research is the joint industry research institute for sugar beet growers and the sugar industry in the Nordic region. NBR conducts:
- Applied research and field trials
- Demonstration projects
- Knowledge transfer to growers and industry stakeholders
The institute collaborates closely with national and European partners to strengthen the productivity, sustainability, and competitiveness of sugar beet cultivation.
Qualifications and Desired Profile
Applicants must have:
- A relevant Master’s degree in Plant Pathology, Agroecology, Crop Science, Molecular Biology, Mathematics, Machine Learning, or a related field
The ideal candidate also has:
- Strong interest in disease epidemiology, IPM, and sustainable agriculture
- Experience with statistical analysis and R programming
- Experience with or interest in machine learning
- Familiarity with PCR or ddPCR techniques (advantage)
- Excellent English communication skills (Danish is a plus)
- Ability to collaborate across academia and industry
- Driving experience, as fieldwork across multiple locations is required
Supervisors
University Supervisors (Aarhus University):
- Associate Professor René Gislum (main supervisor)
- Associate Professor Isaac Kwesi Abuley (co-supervisor)
Industry Supervisors (Nordic Beet Research):
- Anne Lisbet Hansen
- Louise Holmquist
Work Locations
- Nordic Beet Research Foundation:
Højbygårdvej 14, 4960 Holeby, Denmark - Aarhus University:
Forsøgsvej 1, 4200 Slagelse, Denmark
Application Procedure
Mandatory Project Description
- Applications must be submitted via this link to submit your application.
- For technical reasons, applicants must upload a project description as a PDF.
Please copy the project description exactly as provided above and upload it unchanged. - Only applications received before the deadline will be evaluated.
- Shortlisting will be used, and the evaluation committee may request additional information or invite selected candidates for an interview.
- Admission is conditional upon full external funding from Innovation Fund Denmark.
Equality, Diversity, and Employment Conditions
Aarhus University strives to be an inclusive and inspiring workplace. Equality and diversity are considered strengths, and all qualified applicants are encouraged to apply regardless of background.
Salary and employment terms follow applicable Danish collective agreements.
Frequently Asked Questions (FAQs)
Is this position open to international applicants?
Yes, international applicants are welcome, provided they meet the academic and practical requirements.
What makes this an Industrial PhD?
You will be employed by an industry partner (NBR) while completing your PhD at Aarhus University, working equally in both environments.
Is machine learning experience mandatory?
Not mandatory, but strong analytical skills and interest in data-driven modeling are highly valued.
Will fieldwork be required?
Yes. The project involves field trials at multiple locations, so driving experience is important.
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