AI methods in electron and fluorescence microscopy

New generations of electron microscopes and super-resolution fluorescence microscopes, combined with deep learning methods, offer unprecedented possibilities for the analysis of material and biological samples. During the placement, students will learn about reconstruction methods such as deconvolution and super-resolution, and gain insight into methods for automatic segmentation, detection and trekking of large numbers of small objects. You will experience the application of these methods to problems of 2D diffractogram reconstruction, reconstruction in structured illumination microscopy, detection of diffraction gratings, and automatic tracking of subcellular structures in living cells. Knowledge of Python programming is required for the internship. Knowledge of the open source machine learning architecture PyTorch and possibly the MATLAB programming environment is an advantage.

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