The Roeffaers Lab at KU Leuven develops cutting-edge microscopy techniques to address key challenges in environmental science, catalysis, and biomedical research. Our team specializes in fluorescence and Raman-based approaches, integrating advanced microscopic analysis to gain molecular-level insights into complex materials and systems. The lab is internationally recognized for its expertise in correlative imaging and materials science.
To advance our microplastics research, we are looking for a highly motivated Postdoctoral Researcher with strong expertise in fluorescence microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring.
The project focuses on building scalable, accreditation-ready analysis workflows to detect and classify microplastics in complex sample types such as drinking water, plant-based beverages, and biological fluids.
As a key team member, you will:
Develop advanced pipelines for analyzing fluorescence microscopy datasets, integrating spectral, morphological, and lifetime features.
Apply chemometric and machine learning methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification.
Optimize workflows for high-throughput imaging and real-world sample variability, minimizing false positives and maximizing robustness.
Validate the pipeline using diverse and regulatory-relevant samples, supporting future accreditation.
You will work closely with a multidisciplinary team of chemists, materials scientists, and environmental engineers, as well as industrial and governmental stakeholders. The position includes access to state-of-the-art imaging infrastructure, including high-end fluorescence and Raman microscopes, hyperspectral and lifetime systems, and custom-built hardware.
If you are passionate about applying advanced data analysis to real-world environmental challenges, and you are eager to bring fluorescence microscopy and machine learning together to advance microplastic detection, we strongly encourage you to apply.
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Responsibilities
Design and implement chemometric and machine learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify microplastic particles based on spectral and morphological fluorescence data.
Develop and maintain modular analysis pipelines in Python or MATLAB, integrating data preprocessing, feature extraction, and classification for hyperspectral and fluorescence lifetime datasets.
Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence microscopy.
Integrate analysis pipelines with imaging hardware workflows, contributing to software automation for tile stitching, autofocus, and multichannel detection.
Validate models and workflows using diverse, real-world sample matrices (e.g., drinking water, milk, blood), benchmarking against regulatory and ISO guidelines.
Collaborate with a multidisciplinary team of microscopists, materials scientists, and environmental researchers to align data analysis with imaging protocols and sample preparation.
Document, publish, and communicate your work, contributing to scientific publications, stakeholder presentations, and potential valorization or IP development.
Profile
A PhD in data science, applied physics, chemistry, materials science, bioengineering, or a related field, with a strong focus on data-driven analysis.
Proven expertise in processing and analyzing fluorescence microscopy data, with hands-on experience in spectral imaging, lifetime data, or multi-channel image datasets.
Solid background in chemometrics, machine learning, or deep learning, particularly for classification, clustering, or pattern recognition in large datasets.
Proficiency in Python, MATLAB, or similar platforms used for image analysis, data modeling, and algorithm development.
Experience with environmental analysis or microplastic research is a plus but not required.
Strong analytical and problem-solving skills, ability to translate data into insights, and motivation to contribute to a multidisciplinary research and innovation environment.
A publication track record in relevant areas, and a proactive, solution-oriented mindset with an interest in technology valorization and applied research.
Offer
A dynamic research environment with state-of-the-art facilities and close collaborations with industry and governmental stakeholders.
An opportunity to contribute to high-impact environmental and public health innovations, shaping the future of microplastics detection methodologies.
Support for professional development, networking, and career advancement.
Interested?
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