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 September 2025 or later. You can submit your application via the link under 'how to apply'.
Title:
Deep learning and software engineering for next-gen plant phenotyping
Research area and project description:
The Department of Agroecology at Aarhus university has recently acquired state-of-the-art plant phenotyping infrastructure, including a diverse range of UAV sensors for canopy determination, a multispectral camera for root mixture analysis, and a microCT scanner for 3D plant-soil model construction. These technologies provide unprecedented opportunities for non-destructive, high-throughput plant trait analysis. However, exploiting their full potential requires the development of advanced data processing pipelines and accessible software solutions.
This PhD position will play a critical role in unlocking the value of these technologies by developing deep learning-based software solutions that automate root and shoot phenotyping. The successful candidate will create user-friendly tools that enable researchers across disciplines—from soil science to crop breeding—to extract meaningful biological insights from complex imaging datasets.
The successful candidate will design and implement software solutions for three key multimodal plant phenotyping pipelines:
Pipeline 1: UAV image processing and analysis for shoot trait extraction
Development of a versatile software pipeline for processing and analyzing UAV-acquired imagery – including RGB, thermal, LiDAR, and hyperspectral data - to extract key shoot traits related to canopy structure, physiology, and biochemical composition.
Pipeline 2: Multispectral Root Image Analysis for Complex Plant Mixtures
Creation of automated workflows to extract root traits and differentiate plant species from belowground using multispectral root imaging and multivariate statistical analyses.
Pipeline 3: 3D Root Architecture Modelling from microCT Scans
Implementation of deep learning-based reconstruction and segmentation pipelines for 3D root system modelling using micro-computed tomography.
While developing and implementing the pipelines, the candidate will be able to ask several data/plant sciences-related questions that could have not been answered without using the developed tools. These include, but not limited to:
Research Question 1: How can multimodal UAV data (RGB, thermal, LiDAR, hyperspectral) be fused using machine learning to predict complex canopy traits such as water-use efficiency and early stress responses across environments?
Research Question 2: How can machine learning models be trained to accurately identify species and extract trait information from overlapping or intertwined root systems in multispectral images of mixed plant communities?
Research Question 3: How do different deep learning architectures perform in reconstructing and quantifying 3D root system traits from noisy microCT datasets, and which traits are most predictive of performance under varying plant-soil dynamics?
By answering these questions and developing the pipelines, the candidate will contribute to advancing research across several domains, including data science, computer vision, and plant science. Finally, this work will enable researchers to scale up and conduct cost-effective data collection and analysis, opening up new opportunities for novel discoveries and innovations.
Project description (½-4 pages): This document should describe your ideas and research plans for this specific project. If you wish to, you can indicate an URL where further information can be found.
Qualifications and specific competences:
Applicants to the PhD position must have a relevant master’s degree in data science, computer programming, computer vision, computer engineering, computational biology, agricultural engineering, biology or equivalent degree.
Alternatively, students with a great potential and track-record with or in pursuit of a bachelor’s or master’s degree in the mentioned fields will be considered for our 4+4 or 3+5 PhD program. See this link for detail: https://phd.tech.au.dk/for-applicants/phd-study-structure-and-income.
Add-ons:
Place of employment and place of work:
The place of employment is Aarhus University, and the place of work is AU Viborg, Blichers Allé 20, DK-8830 Tjele, Denmark.
Contacts:
Applicants seeking further information regarding the PhD position are invited to contact:
For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:
How to apply:
Please follow this link to submit your application.
Application deadline is 01 June 2025 at 23:59 CEST.
Preferred starting date is 01 September 2025.
Please note:
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.
