Jobs

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.

Your tasks:

• 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.

Prerequisites

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.

The research group of Computational Omics and Precision Oncology, led by Dr. Junyan Lu, at Heidelberg University Hospital offers two fully funded positions for PhD Students in the area of bioinformatics and biomedical data science. Dr. Lu’s team works closely with the partners within the SMART-CARE consortium, which 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. 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. Your primary research goal is to identify robust and translatable biomarkers for tumor progression, recurrence, and treatment response through analyzing and integrating high-throughput omics datasets (e.g. proteomics, metabolomics, transcriptomics, genomics and etc.) together with clinical data. You will have the opportunity to shape your own research profile by pursuing research in method development and collaborative analysis of novel datasets.

Your tasks:

• Perform in-depth analysis on each individual omic dataset and conduct integrative data analysis on multi-omics datasets using various computational tools (e.g. statistical inference, network modeling, machine learning and etc.) to identify features that can explain the heterogeneity in clinical outcome and treatment response.
• Interpret the biological meaning of the identified features and evaluate the potential of those features as candidates for robust clinical biomarkers.
• Collaborate with clinical and experimental groups in SMART-CARE to implement your computational methods and biological insights into clinical practice and decision making.
• One of the two PhD positions will focus more on multi-omics data analysis/integration method/software development and the other one will focus on biological discovery and interpretation.
• Publish your computational methods and biological discoveries in scientific articles, and publish scientific software as, e.g., R/Bioconductor packages.

Prerequisites

• Master’s degree or equivalent degree in bioinformatics, informatics, computational biology, or related subjects.
• Good programming skills in at least one programming language, preferably in R.
• Good knowledge and understanding of the concepts of cancer biology, personalized medicine, and biomarker discovery.
• Experience with the analysis of high-throughput omics datasets (e.g proteomics, transcriptomics, genomics and etc.) and interest in implementing machine learning and deep learning models is a bonus.
• You are enthusiastic about 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.

We are looking for a data engineer/technical assistant to work within the SMART-CARE consortium, which aims at advancing 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. 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 the position is to establish and maintain the data analysis framework for pre-processing, quality control, documentation, and reporting of clinical omic data, with the focus on mass spectrometry proteomics and metabolomics data. You will also have the opportunity to shape your own research profile by pursuing research in developing innovative methods from mass spectrometry data analysis and integration.

Your tasks:

• Assemble suitable computational tools (or develop novel tools when necessary) into a streamlined and interoperable computation framework for end-to-end processing, QC, and reporting of clinical mass-spectrometry data. Ensure the robustness, reproducibility of the framework.
• Work closely with the research team and partners from the SMART-CARE consortium to integrate tools developed within the consortium into the framework and provide a user-friendly interface for internal and potential external users.
• Support the research team in locating and using relevant data resources, and in ingesting newly produced data.
• Assist the group leader in maintaining the research infracstructure for the group.
• Participate in project/data management tasks, including collaborative data production, analysis, and presentation.

Prerequisites

• Bachelor’s degree (or higher) in bioinformatics, informatics or computer science.
• Strong programming skills in R and/or Python, as well as the ability to acquire proficiency with modern big-data management systems (cloud, object storage) and with data analytical tasks.
• The tasks will also require the programmatic construction of web interfaces, for instance using tools such as Rmarkdown and shiny.
• Have good organizational and interpersonal skills. Fluent in German is a plus.
• Interests in computational algorithms, statistical analysis, or biological discovery are a plus.