- Veröffentlicht: Dienstag, 18. Februar 2020 08:37
The research group of Wolfgang Huber (www.huber.embl.de) 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 has strong collaborations 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, that 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 therefore a major global health problem. The surge of genomic technologies in clinical practice has brought great benefit in disease stratification, however genomic information alone is often insufficient to accurately predict or 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 biomarkers that could be used to tailor personalized cancer therapy. There is a need to develop and establish powerful and standardizable pipelines for proteome and metabolome analysis in the clinical setting. The aim of the SMART-CARE project is to firmly establish mass spectrometry-based systems medicine technologies and data analysis methods and apply these to predict cancer recurrence across paradigmatic tumor entities and thus improve patient stratification.
You will be part of the Huber group at EMBL’s Heidelberg site, participate in the SMART-CARE project and shape your own research profile by pursuing research in method development and collaborative analysis of novel datasets. Your activities will comprise the following:
· Advanced statistical inference and biological model building: Compare the information content of the different data types, in terms of their ability to map patient and disease heterogeneity, and predict tumor recurrence and treatment response. Use and adapt methods for data integration developed within the group, e.g. the Multi Omics Factor Analysis (MOFA) or further develop state of the art methods from statistical learning.
· Data-type specific “preprocessing” and feature engineering.
· Automated data quality assessment / control (QA/QC) 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 a quantitative science (mathematics, statistics, physics, computer science, computational biology). We are looking for a range of talents, which should include some of the following: solid training in mathematical statistics, understanding of high-dimensional statistics, machine learning and Bayesian approaches; experience in biological data science and data-driven discovery; scientific programming, and good software engineering skills. Applications from “newcomers” into biology are welcome.
You might also have
You are excited by making or contributing 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 interested in understanding and comparing methods in computational biology and in pushing them forward with your own ideas.