Post-Doc for bioinformatics and mult-omics analysis in Biomedical research
pThe 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. /pp(1) /pp(2) /pp(3) /ppThe 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. /ph3Tasks /h3ulliData preprocessing of a large proteomics data set generated by high-throughput mass spectrometry. /liliExploratory data analysis and data normalization. /liliPerform statistical data analysis and apply machine learning to identify protein markers characteristic of health states and trajectories. /liliDissemination of scientific results through scientific publications and contributions to scientific events. /li /ulh3Requirements /h3ulliPhD 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. /liliExperience in the analysis, quality assessment and normalization of quantitative omics data, preferentially mass spectrometry proteomics. /liliExtensive experience in data analysis with R and/or Python. /liliExperience in reproducible research. /liliKnowledge in statistical data modeling, multiple regression analysis and machine learning. /liliProactivity and the ability to solve problems independently. /liliAbility to work in a distributed, multidisciplinary research environment. /liliExcellent written and oral communication skills in English. /li /ulpTagged as: Data Science, Life Sciences /p #J-18808-Ljbffr
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. /ph3Tasks /h3ulliData preprocessing of a large proteomics data set generated by high-throughput mass spectrometry. /liliExploratory data analysis and data normalization. /liliPerform statistical data analysis and apply machine learning to identify protein markers characteristic of health states and trajectories. /liliDissemination of scientific results through scientific publications and contributions to scientific events. /li /ulh3Requirements /h3ulliPhD 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. /liliExperience in the analysis, quality assessment and normalization of quantitative omics data, preferentially mass spectrometry proteomics. /liliExtensive experience in data analysis with R and/or Python. /liliExperience in reproducible research. /liliKnowledge in statistical data modeling, multiple regression analysis and machine learning. /liliProactivity and the ability to solve problems independently. /liliAbility to work in a distributed, multidisciplinary research environment. /liliExcellent written and oral communication skills in English. /li /ulpTagged as: Data Science, Life Sciences /p #J-18808-Ljbffr