The MeBioS Biophotonics group at KU Leuven offers a unique opportunity for a PhD researcher to advance data-efficient artificial intelligence for industrial process monitoring. This multidisciplinary team integrates biophotonics, advanced imaging, spectroscopy, and cutting-edge data analytics to address real-world challenges. The group’s research spans from developing lab-based prototypes and algorithms to deploying solutions in industry-relevant settings, with a strong emphasis on AI-driven applications for inline quality control, process monitoring, and data-efficient model maintenance. While the primary application domain is the agrifood industry, the methodologies developed are broadly applicable to adjacent sectors such as chemicals and pharmaceuticals. The successful candidate will join a collaborative environment that values hands-on research, teamwork, and excellence. The PhD position is part of a multi-partner project involving academic labs in Flanders and leading companies in pharma, chemicals, and consumer goods. The research focus will be on maintaining trustworthy AI models over time and minimizing the effort required for (re)labeling data. Industrial cases will serve as primary test beds, but the developed methods are expected to be validated in broader fields, including agrifood. Supervision will be provided by Prof. Wouter Saeys and Dr. Bart De Ketelaere, with additional support from industrial partners. The position offers a full-time contract for one year, with the possibility of extension to four years upon positive evaluation according to the Arenberg Doctoral School regulations. The research environment is dynamic and supportive, providing strong mentorship and opportunities for personal development through training, workshops, seminars, and international conferences. Doctoral training is structured within the Leuven Arenberg Doctoral School. Applicants should hold (or be close to completing) a Master’s degree in Engineering (Bioscience, Electrical/Computer, Mechanical), Computer Science, Applied Mathematics/Statistics, Physics, or a related discipline. Candidates graduating soon are also welcome. Essential skills include a solid foundation in machine learning and deep learning, proficiency in Python, and interest in computer vision, representation learning, concept drift, Statistical Process Monitoring, active learning/DOE, and MLOps. The group seeks candidates who are curious, independent, and possess excellent communication skills in English, as well as enthusiasm for working in multidisciplinary, multi-partner teams. Remuneration is according to KU Leuven salary scales (Scale 43), and the position includes comprehensive doctoral training and personal development opportunities. The expected start date is March 1st, 2026. Applications are accepted via the KU Leuven online application tool and are screened on a rolling basis, so early submission is encouraged. Applicants should provide a motivation letter (maximum 1 A4 page) outlining their motivation and alignment with the project, earliest possible start date, and a detailed CV. For further information, candidates may contact Dr. Bart De Ketelaere or Prof. Wouter Saeys via email. KU Leuven is committed to fostering an inclusive, respectful, and socially safe environment, embracing diversity and equal opportunity for all. The university does not tolerate discrimination based on gender identity, sexual orientation, age, ethnicity, skin colour, religious or philosophical beliefs, neurodivergence, disability, health, or socioeconomic status. Accessibility and support are available for applicants who require assistance.

The MeBioS research unit at KU Leuven is offering a PhD position focused on the development of laser scatter imaging techniques for early detection of plant stress in greenhouse crops, primarily tomatoes. The research is embedded within the Biophotonics group, which specializes in innovative optical sensor technologies and advanced data analysis methods to monitor and control the quality of biological products and processes, with a strong emphasis on applications in precision agriculture. This project aims to design and implement a camera-based multispectral laser scattering imaging setup, optimized for measuring plant leaves. By capturing and analyzing laser scatter images at different wavelengths, the research will untangle the effects of light scattering and absorption, which are linked to plant microstructure and pigmentation. The ultimate goal is to develop a functional prototype sensor capable of detecting subtle stress responses in plants, enabling timely adjustments in greenhouse management for optimized crop yield and resource use. The successful candidate will be responsible for developing the imaging setup, analyzing scatter images to extract relevant plant physiological information, and validating the sensor prototype in both laboratory and greenhouse conditions. The project involves interdisciplinary collaboration, combining expertise in optics, photonics, machine learning, computer vision, and crop science. Applicants should hold a MSc degree in Engineering (bioscience, electrical, optical) or Science (physics, photonics), possess strong analytical and hands-on skills, and be proficient in programming (Matlab/Python for data processing, Labview for control software). Excellent communication skills and fluency in English are required. The position offers a full-time contract for one year, renewable up to four years upon positive evaluation, with remuneration according to KU Leuven salary scales. The research environment provides state-of-the-art infrastructure, high-level scientific training, and the opportunity to work in a dynamic, multidisciplinary team at a top-ranked university. For further information, candidates may contact Prof. dr. ir. Wouter Saeys or dr. ir. Annelies Postelmans. Applications should be submitted via the KU Leuven jobsite before the deadline. KU Leuven is committed to diversity, inclusion, and equal opportunity in its academic community.