The internship aims to deliver the proof of principle that FAST scaffolds are relevant to deliver highly multiplexable fluorescent labels. The project involves numerically generating multispectral fluorescence responses of FAST scaffolds, exploiting machine learning tools for delivering kinetic fingerprints, exploring sequences of modulated illumination for optimizing separability, and implementing optimal illumination sequences experimentally for highly multiplexed imaging. The work is embedded in a collaboration with Sorbonne University and Leuven University.