Courses
Based on the cutting-edge research performed at CPR, the center carries out undergraduate teaching for bachelor and master students and postgraduate education and supervision at the University. Get an overview of where you as a student can meet us:
Master courses
Learn a range of methods for finding, analyzing and integrating heterogeneous biological data in the context of a specific disease, and to critically evaluate results of such analyses.
Learn to analyze and process large biological data sets by writing and running Python programs.
Get an introduction to cutting-edge cellular, molecular, and computational approaches to investigation of disease-related proteins, stem cell biology; regulation of metabolism, and cell factory engineering.
Professional master's programmes
(in Danish)
Forstå hvordan forskellige typer real-world data, alt fra genetik til app-indsamlede miljøpåvirkninger, kan bidrage til bedre diagnose, prognose og personlig behandling. Kurset fokuserer på klinisk afprøvning af datadrevne metoder og beslutningsstøtteværktøjer samt inddragelse af patienter
PhD courses
Get training in how to analyze data and think critically about state-of-the-art techniques in cellular, molecular, and computational biology characterizing protein mechanisms of action in living cells.
Gain insights into the major high-end proteomics technologies and workflows and learn to design experiments and analyze data.
Learn central aspects of omics studies in order to design studies and critically evaluate results from studies of genomics, transcriptomics, proteomics, metabolomics and bioinformatics in humans.
Postdoc courses
Get an overview of the major high-end quantitative proteomics technologies and their applications in biology.
Learn advanced research methodologies to characterize protein mechanisms focusing on gene editing and cryo-EM.
Learn how to get data access to big data of various types and of a range of methods, including machine learning and biological network analysis, for finding, analyzing and integrating heterogeneous data in the context of a specific disease.
MOOC