Description

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.

Application deadline: Feb. 24, 2026

Program Begins Spring 2026

https://www.nature.com/naturecareers/job/12852785/postdoc-in-chemometrics-and-machine-learning-for-fluorescence-imaging/