We are looking for a postdoctoral researcher to work within the SMART-CARE consortium to advance precision oncology through mass-spectrometry proteomics/metabolomics data analysis and biomedical data integration. SMART-CARE consortium is a collaboration between University Hospital Heidelberg, Heidelberg University, Mannheim University of Applied Sciences, the German Cancer Research Centre (DKFZ) and European Molecular Biology Laboratory (EMBL). It aims to develop novel systems medicine approaches to battle cancer recurrence, using the integration of high-quality proteome and metabolome mass spectrometry data with other ‘omics and clinical data. 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. The SMART-CARE consortium brings together clinical, mass spectrometry and computational expertise to tackle the urgent need for developing and establishing powerful and standardisable methods for proteome and metabolome analysis, as well as leveraging the power of multi-omics and machine learning to achieve high precision and robustness. The primary goal of this position is to process and analyse proteomic and metabolomic datasets, together with other ‘omic and clinical data types, for the purpose of identifying biomarkers for predict tumour 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.
- Establish and apply computational framework for processing clinical mass spectrometry data (proteomics and metabolomic) to acquire analysis-ready data with high-resolution.
- Perform and automate data quality control, particularly sample metadata based QC, on the mass-spectrometry data, in order to identify and annotation potential technical confounders for down-stream analyses.
- Integrate MS data with other omics (genomics, transcriptomics, epigenomics and etc.) and clinical data to infer biological and clinically meaningful information that can guide patient stratification and biomarker development.
- Collaborate with clinical and experimental groups in SMART-CARE to implement your computational methods and biological insights into clinical practice and decision making.
- Publish your 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 biology (computational biology, bioinformatic or biostatistics).
Profound skills in processing and analysing mass spectrometry data.
Strong programming skills in R and/or Python.
Experience with implementing machine learning and/or deep learning models for integrative multi-omic data analysis is a plus.
You are enthusiastic in data science and advancing cancer research. You enjoy collaborative work and like to communicate concepts and results to other scientists in different fields of research.