Mass spectrometry-based proteomics is one of the most powerful technologies for studying the cellular proteome, protein interactions, and post-translational modifications. Although several proteomics workflows are well-established methods, the field is fast evolving with new acquisition approaches and generating of high-content data structures. This evolution has expanded the number of applications, and mass spectrometry-based proteomics has become one of the principal platforms in the multi-omics universe.
The implementation of new experimental possibilities also requires the development of interactive and highly-visual tools for the integration and representation of proteomics data with other omics datasets. Addressing these different needs is crucial to use proteomics at its full potential and thus deliver key contributions in our understanding of biological processes to support the health sector with valuable information.
The objectives during the PhD are to improve existing and build new learning models for the prediction of MS-based proteomics related peptide and protein properties. These will be integrated into the MaxQuant software to improve the identification and quantification of peptides and proteins. Prediction targets include fragmentation spectra, collision cross-section, retention time, and many more. The aim is to integrate these improvements into the DDA, DIA, and top-down modules of MaxQuant.
The work involves Methodology algorithm development in the C# based MaxQuant environment and making use of the machine and deep learning libraries such as TensorFlow and Microsoft CNTK as well as Team-based programming in the Cox lab continuous integration environment.
Required Skills are excellent programming skills and good command of English Background knowledge in biology or mass spectrometry is a plus.
Tagged as: Computer Science, Life Sciences
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