The MeBioS (Mechatronics, Biostatistics and Sensors) division at KU Leuven is a dynamic, multidisciplinary research group at the interface of agriculture, sensing technologies, and artificial intelligence. Our research focuses on turning complex biological and environmental data into actionable insights for sustainable production systems. Within MeBioS, the Biophotonics group designs and deploys advanced sensing systems, including imaging and optical technologies, and develops data‑driven and AI‑based methods to support monitoring, prediction and decision‑making. A strong focus lies on applications in the agri‑food sector, where we closely collaborate with practice centres, technology providers and industry partners.
AI plays an increasingly central role in our research, particularly for automated monitoring, context‑aware prediction, and efficient model maintenance in real‑world environments. In the INTEGRAI project, MeBioS coordinates a multi‑partner initiative that aims to move beyond simple detection or counting of insects on sticky plates towards predictive, ecologically informed decision support for integrated pest management (IPM).
The prospective candidate will be embedded in a collaborative environment that values strong fundamental research, interaction with end users, and translation of AI methods to practical impact. Close collaboration with partners from KU Leuven (DTAI), Flemish practice centres and sector stakeholders is a core part of this project.
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Project
We are recruiting a PhD researcher within the INTEGRAI project, a large multi‑partner, practice‑driven research project funded within the VLAIO LA‑traject framework. The project focuses on the development of a context‑aware, predictive AI platform for insect monitoring and integrated pest management in agriculture, with initial focus on Belgian endives and carrots. As a PhD researcher, you will contribute to the design of advanced AI methods that combine computer vision outputs from insect trap imagery, spatial, temporal, ecological and crop‑specific context, and human‑in‑the‑loop feedback from experts and practice centres. Your research will focus on making AI systems robust, interpretable and data‑efficient over time, enabling reliable risk prediction and decision support under changing environmental conditions.
Profile
- A Master’s in Engineering (Bioscience, Electrical/Computer, Mechanical), Computer Science, Applied Math/Statistics, Physics—or related. Candidates who will graduate in the near future are also welcome to apply.
- Strong foundation in machine learning/deep learning and solid Python skills.
- Interest in computer vision, representation learning, context-aware AI, active learning/DOE, MLOps.
- Curiosity, independence, and excellent communication in English. Speaking Dutch is a plus, but not required.
- Enthusiasm to work with multi disciplinary, multi partner teams in academia and industry.
Offer
- A full-time position for 1 year. After positive evaluation according the Arenberg Doctoral School regulations, the contract will be extended to allow completion of the PhD in 4 years.
- A varied and challenging job in close cooperation with industry.
- A dynamic and supportive research environment providing strong mentorship.
- Opportunities for further personal development through training and participation in workshops, courses, seminars, and international conferences. Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd).
- Expected start date: October 1st 2026.
- Remuneration according to the KU Leuven salary scales (Scale 43, https://admin.kuleuven.be/personeel/english/salary/salary_ap.html).
Interested?
Candidates are invited to submit their application via the KU Leuven online application tool no later than June 30th, 2026. Please note that applications will be screened on a rolling basis, and suitable candidates may be selected and interviewed before the deadline. Early applications are therefore strongly encouraged. Documents to provide:
- Motivation letter (maximum 1 A4 page), outlining your motivation for the position and how your strengths and background align with the project. Also include your earliest possible start date.
- Curriculum Vitae, including details of your education, current position, relevant work experience (if any), relevant extracurricular activities, and references we may contact.
For more information please contact:
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