Post-Doc for bioinformatics and mult-omics analysis in Biomedical research

The Eurac Research Institute for Biomedicine (IfB) located at Bolzano’s NOI Techpark, is dedicated to improving human health through precision prevention research. The interdisciplinary work is focused on the investigation of genetic, biological, environmental and lifestyle factors in order to predict, slow or prevent human disease. The institute established the CHRIS population study [1], a decade-long collaboration with the South Tyrolean Healthcare Service, providing extensive data and biological materials suitable for such research. The IfB employs state-of-the-art labs, scientific computing resources and interdisciplinary expertise dedicated to the investigation of neurological, cardiovascular, and metabolic health. Within the HEALTH3P initiative, conducted jointly with Charité – Universitätsmedizin Berlin (Prof. Dr. Markus Ralser, Biochemistry and Systems Biology of Metabolism Group and Prof. Dr. Britta Siegmund, Department of Gastroenterology, Infectious Diseases and Rheumatology) we are investigating molecular markers for health in the population, with a focus on cardiovascular, kidney and metabolic health. HEALTH3P represents a foundational step in a broader partnership dedicated to investigate the active processes maintaining health and to develop precision-based approaches for disease prevention.

The successful candidate will join the HEALTH3P team, and will process and analyze large-scale mass spectrometry-based proteomics datasets [2,3] to investigate molecular signatures for health and health trajectories and to develop predictive models for health outcomes. The work is a close collaboration with Charité – Universitätsmedizin Berlin. The candidate will work together with senior researchers at both IfB and Charité.

[1] https://doi.org/10.1093/ije/dyaf064 
[2] https://doi.org/10.1038/s41587-021-00860-4 
[3] https://doi.org/10.1038/s43856-025-00856-0

Tasks:

• Data preprocessing of a large proteomics data set generated by high-throughput mass spectrometry. 
• Exploratory data analysis and data normalization.
• Perform statistical data analysis and apply machine learning to identify protein markers characteristic of health states and trajectories.

• Dissemination of scientific results through scientific publications and contributions to scientific events.

  Requirements:

  • PhD in a relevant field of research (Bioinformatics, Biostatistics, or Computational Biology), with typically 0–3 years of relevant postdoctoral experience at the time of appointment.
  • Experience in the analysis, quality assessment and normalization of quantitative omics data, preferentially mass spectrometry proteomics.
  • Extensive experience in data analysis with R and/or Python.
  • Experience in reproducible research.
  • Knowledge in statistical data modeling, multiple regression analysis and machine learning.
  • Proactivity and the ability to solve problems independently.
  • Ability to work in a distributed, multidisciplinary research environment.
  • Excellent written and oral communication skills in English.

    See the link in the job posting for more information, details and how to apply.