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 July 2023 or later.

Title: PhD in machine learning and agricultural ecology: hierarchical deep learning for better automatic pest classification and biodiversity monitoring in agroecosystems

Research area and project description:
This PhD project aims at developing novel algorithms and annotation tools for hierarchical classification, which has profound and immediate applications to both agriculture and ecology.


Recently, remarkable innovations in deep learning have begun to transform agriculture – arguably, the most impactful human activity on earth. If used appropriately, machine learning and AI have the potential to drive a digital green transition that could make agriculture more sustainable, resilient and productive.

One increasingly important data-science task in agriculture is image classification, which is crucial for detecting, monitoring, and eventually mitigating the impact of weeds, pests and diseases. When applied to agricultural problems, image classification generally suffers from three important limitations: 1. low generalisability – i.e., it translates poorly to new contexts –; 2. data-hunger – i.e., large training datasets are required –; and 3. the lack of accessible implementations for practitioners.

As an alternative to the conventional “flat” classification, we propose to take advantage of the underlying hierarchical taxonomies of the predicted classes to constrain them in a priori trees, a currently underexplored area. As a simplistic illustration, instead of considering oranges, apples and lemons as independent, we may formulate the explicit tree: {{orange, lemon}, apples}, i.e. grouping citrus fruits, and embed this representation in the structure of a neural network.


The successful applicant will use already available datasets to explore hierarchical classifications and test our hypotheses: increased prediction generalisability and network robustness as well as reducing the amount of data required. This project aims to produce both seminal machine-learning concepts and free and open-source tools for real-world problems.

Research Group:

The successful student will integrate the growing Digital Approaches for Resilient and Sustainable Agriculture (DARSA) group, started in 2022, at the Center for Quantitative Genetics and Genomics. Both the group and the centre are inclusive and multidisciplinary environments with a range of local and international collaborations. The research takes place at Aarhus University, a world-leading institution located in a vibrant city.

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:
The applicant must:

  • Hold a Master’s degree in data science, mathematics, physics, computational biology or another quantitative field
  • Be at least familiar with deep learning, image processing and general database architecture
  • Have strong programming skills (in particular, experience working with scientific languages (e.g. R, Python) and packages (e.g. Pytorch, Tensorflow, Scikit-learn, Numpy, Pandas and OpenCV)
  • Have an interest in digital and sustainable agriculture and ecology
  • Be fluent in English (written and spoken)
  • Demonstrate advanced collaborative and interpersonal skills

Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is C. F Møllers Allé 3, Bygning 1130, 8000 Aarhus C., Denmark.

Applicants seeking further information are invited to contact:

How to apply:
Please follow this link to submit your application. Application deadline is 20 December 2022 23:59 CET. Preferred starting date is 1 July 2023.

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.   

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