Frank Kjeldsen

A fully funded PhD position is available in experimental proteomics at the University of Southern Denmark, supervised by Professor Frank Kjeldsen. The research focuses on developing and applying negative ion mode LC-MS/MS-based peptide analysis to uncover new molecular insights and expand the scope of proteome characterization. The project combines method development, mechanistic exploration, and downstream biological applications, challenging the current paradigm that overlooks acidic biomolecules in proteomics. The successful candidate will join the internationally recognized Protein Research Group (PRG), working in a collaborative environment with over 40 researchers, PhD students, and postdocs. The lab offers access to advanced instrumentation such as Orbitrap Eclipse, Orbitrap Astral, and TIMSTOF Ultra2. Applicants must have an MSc in a relevant field and hands-on experience with mass spectrometry-based proteomics. Desirable skills include experience with negative ion mode MS, computational proteomics, and programming for data analysis. The position provides competitive salary, social benefits, and support for relocation and visas. Applications are invited via the university's career site, with further details available through the provided link.

University of Southern Denmark (SDU), Department of Biochemistry and Molecular Biology, is recruiting a Postdoctoral Researcher in Computational Proteomics and Machine Learning in Odense, Denmark. The project sits at the interface of computational proteomics , machine learning , and mass spectrometry , with a focus on peptide ionization, fragmentation in LC-MS/MS, and large-scale tandem MS/MS dataset analysis. The successful candidate will join the Protein Research Group (PRG), an interdisciplinary environment with eight PI-led teams and more than 50 researchers and students. Research topics include peptide fragmentation analysis, spectral interpretation, peptide chemistry, gas-phase ion behavior, post-translational modifications, reproducible computational pipelines, and data-driven modeling of experimentally curated mass spectrometry data. The role involves developing and evaluating machine learning workflows, curating datasets, building predictive models, and collaborating closely with experimental researchers generating high-quality MS data. Applicants must hold a PhD or equivalent in computational proteomics, bioinformatics, machine learning, computer science, chemometrics, analytical chemistry, or a closely related field. Strong Python and ML/deep learning experience are required, along with experience in data preprocessing, curation, and quality control for large datasets. Interest in the chemistry-computation-mass spectrometry interface is essential; experience with proteomics, neural networks, explainable AI, FAIR workflows, and scalable pipelines is an advantage. The appointment is for 2 years with the possibility of a 1-year extension . Salary and benefits follow Danish collective agreements and include pension; support for international candidates is mentioned. Applications must be submitted electronically via the Apply now portal and should include a motivation letter, CV, degree certificates/transcripts, and reference contact information. Deadline: 2026-11-17.