This project focuses on the development, validation, and integration of non-destructive plant sensing technologies, enabling data-driven—and ultimately autonomous—crop management in greenhouse and vertical farming systems. The goal is to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data, and crop models, the aim is to enable more precise and sustainable crop management.
You will work with colleagues on plant genetics, growth strategies, and crop management with the aim of achieving optimal crop performance by integrating plant, environment, and management. The position offers close collaboration with advanced computer science and sensor engineering departments, creating a highly interdisciplinary research environment at the intersection of plant science, engineering, and data science.
What you do
In this position, you will work within a European collaborative project focused on developing advanced sensing and modeling approaches for Controlled Environment Agriculture systems. Your research will involve the deployment of:
- non-invasive sensors to monitor canopy light interception in greenhouse environments
- spectral sensing in the visible and near-infrared (NIR) range to estimate sugar and starch content, chlorophyll levels, and plant water status
- AI-based computer vision systems to monitor crop development
- integrated sensing frameworks that combine multiple data streams into efficient decision-support tools for growers
The data collected through these approaches will be integrated into crop growth and transpiration models and combined with other monitoring systems. Initial experiments will be conducted in university growth chambers and greenhouse facilities, followed by validation in commercial-scale greenhouse operations. You will work in close collaboration with the Sensor Engineering department and the Department of Advanced Computer Science.
Beyond your core research activities, you will contribute to ongoing projects within a research institute and collaborate across disciplines in areas such as data acquisition and analysis. These projects span topics including the relationship between food production, environmental impact, and human health, as well as developments in agricultural robotics and automation. You will have the opportunity to contribute to teaching within a newly established MSc programme in Crop Biotechnology and Engineering.
Profile
We are looking for a creative and proactive researcher with a strong interest in the future of digital and autonomous agriculture. You have a clear vision of next-generation Controlled Environment Agriculture systems and feel comfortable working in a dynamic, interdisciplinary environment. You enjoy working with people from different disciplines and are able to contribute effectively to collaborations with, for example, crop physiologists and plant scientists.
What they ask
- A PhD in a relevant field, such as plant science, crop science, agricultural engineering, biosystems engineering, horticulture, applied physics, data science or a related discipline
- Demonstrable research experience in at least one of the following areas: crop modelling, plant sensing or data-driven crop management
- Demonstrable experience in translating research insights into practical applications and collaborating with academic and industry partners in the context of Controlled Environment Agriculture systems
- Experience with Controlled Environment Agriculture (CEA), greenhouse cultivation or another relevant controlled growing environment
- Experience in teaching, outreach, or international research networks is considered an advantage
- Excellent command of English, both written and spoken (minimum C1 level, CEFR)
- Proficiency in Dutch is considered an advantage
Practical
- Regular travel to locations across the Randstad region in the Netherlands and Belgium is part of this position (at least once a week).
How to apply
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