Researcher in large-scale multi-omics data analysis

The Institute for Biomedicine at Eurac Research is seeking a highly motivated researcher to join the Biomedical Informatics Group.

The Institute for Biomedicine is home to an international and interdisciplinary team of scientists, students and support staff, integrated in an excellent network of international and national partners. Living and working in Bozen/Bolzano allows you to experience a unique combination of alpine and Mediterranean culture right at the heart of one of the most exciting mountain regions in Europe.

The Institute for Biomedicine is running the Cooperative Health Research in South Tyrol (CHRIS) population study, focusing on the investigation of the cardiometabolic and neurological health of 13,000 general population subjects [1] that excels with a high participation rate and a rich genomic, metabolomic and proteomic collection. Targeted (Biocrates p180 kit-based) metabolomics data for 7,000 participants were generated and are now actively analyzed [2]. HILIC LC-MS-based untargeted metabolomics data were acquired as well for the same participants. More recently we have obtained both mass spectrometry-based [3] and affinity-based (SomaScan) plasma proteomics. The SomaScan collection provides measurements for up to 7,000 proteins in 4,250 individuals.

The successful candidate will investigate multi-omics features associated with different health related traits. The candidate will also apply machine learning methods to generate predictive models for these different health conditions to be applied for stratification in precision health applications. The candidate will work in close coordination with other senior researchers in the multi-omics analysis.

[1] https://doi.org/10.1186/s12967-015-0704-9

[2] https://doi.org/10.3390/metabo12030205

[3] https://doi.org/10.1101/2023.10.11.23296871

Tasks:

• Explore confounding factors in SomaScan proteomics data, evaluate relationships between health traits/phenotypes of interest and medication, environmental and life-style factors.
• Evaluate omics features associated with health traits/phenotypes adjusting for the confounding factors.
• Apply machine learning methods to generate predictive models for health conditions and investigate characteristic omics signatures.
• Coordinate analyses and collaborate with other senior researchers to analyze the available multi-omics data.
• Dissemination of scientific results through conference contributions and scientific publications. 
   

Requirements:

• PhD within relevant field of research (Bioinformatics, Biostatistics, Computational Biology).
• Experience in large scale data handling and processing, including statistical data analysis and machine learning.
• Proficiency in R and/or Python software development.
  Experience in the biomedical field, e.g., in cardiometabolic-related research is preferred.
• Proactivity and ability to solve problems independently.
• Ability to work in a multi-disciplinary research environment.
• Good communication skills; fluent in the English language.
   

We offer: 

• A collaborative work environment with multidisciplinary teams of experts from different fields of biomedical research.
• Access to cutting-edge omics and health data and computational resources.
• Attractive living and working conditions.