This advanced bioinformatics practical project focuses on single-cell proteomics (Figure 1), aiming to develop and improve computational methods for mass-spectrometry-based proteomics under low-input and high-noise conditions. Students work on extending state-of-the-art proteomics workflows and machine-learning models to improve spectrum rescoring, peptide identification, and accurate peptide and protein quantification in single-cell data. The project combines (1) methodological development - such as enhancing fragment ion intensity prediction models that generalize across instruments and experimental settings - with (2) downstream analysis, including quality control and cross-omics comparison to single-cell transcriptomics. Through hands-on work with real single-cell proteomics datasets, participants gain experience in Python-based software development, machine learning for proteomics, and collaborative computational research that links technical innovation with biological insight. Students pick one of the two projects at the start of the course. Please check both subprojects for more details!