About the team/job
The research group of Wolfgang Huber (www.huber.embl.org) at EMBL works on biological data science and mathematical / computational method development. The interdisciplinary and international team uses statistical data analysis and modelling to discover and understand biological principles and biomedical applications and collaborates with researchers in basic biology and cancer research.
About the project SMART-CARE
The SMART-CARE consortium is a collaboration between EMBL, the German Cancer Research Centre, the University Hospital Heidelberg, Heidelberg University and Mannheim University of Applied Sciences. It aims to develop new systems medicine approaches to battle cancer recurrence, using the integration of proteome and metabolome mass spectrometry approaches with other ‘omics and clinical data. The consortium brings together clinical, mass spectrometry and computational expertise.
Cancer recurrence is the main determinant of cancer related death and a major global health problem. The surge of genomic technologies in clinical research has brought great benefits in disease stratification, yet genetic information alone is rarely sufficient to accurately identify the dynamic process of disease progression. Systematic analyses of proteins and metabolites using mass spectrometry in combination with statistical data analysis and mathematical modelling promise a new generation of disease stratifications and biomarkers that could be used to tailor precision cancer therapies. To this end, there is a need to develop and establish powerful and standardizable methods for proteome and metabolome analysis. The aim of the SMART-CARE project is to establish mass spectrometry-based systems medicine technologies and data analysis methods in the clinical setting.
You will be part of the Huber group at EMBL Heidelberg and participate in the SMART-CARE project. The primary goal of the position is to evaluate proteomic, metabolomic and other ‘omic data types in terms of their ability to explain patient and disease heterogeneity, and to predict tumor recurrence and treatment response. You will have the opportunity to shape your own research profile by pursuing research in method development and collaborative analysis of novel datasets. Your activities will comprise:
- Advanced statistical inference and biological model building; Use, adapt, or further develop methods for data integration (incl. Multi Omics Factor Analysis, MOFA) and statistical learning.
- Find and establish optimal approaches for data-type specific preprocessing and feature engineering.
- Perform and automate data quality control and visualization.
- Publish computational methods and biological discoveries in scientific articles, and publish scientific software as, e.g., R/Bioconductor packages.
A PhD or equivalent qualification in quantitative sciences (mathematics, physics, quantitative biology and related fields).
You are interested in data science, statistics, scientific computing, and cancer. You are able to assess, adapt or further develop computational methods and wish to make or contribute to biological discoveries. You are interested in interdisciplinary science, enjoy collaborative work and like to communicate concepts and results to other scientists in different fields of research. You are excited by pushing science forward with your own ideas.
For additional information and the instruction on how to apply, please click the Apply button above.
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