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Center for Molecular Fingerprinting (CMF) in Germany seeks an experienced MS-based omics data processing scientist to lead raw data preprocessing and QC workflows for large-scale metabolomics and proteomics analyses.
You'll develop automated pipelines, apply untargeted LC-MS processing, feature detection and QC strategies, and collaborate with laboratory scientists to ensure high-quality datasets ready for biological interpretation.
Join a high-throughput mass spectrometry (MS)-based omics platform analyzing more than 100,000 human plasma samples as part of a major European population health initiative.
The Center for Molecular Fingerprinting (CMF, www.cmf.hu ), led by the 2023 Nobel Laureate in Physics Prof. Dr. Ferenc Krausz, is an interdisciplinary, nonprofit research institution. We are an international team of laser scientists, molecular biologists, medical doctors, engineers, and data analysts who came together driven by a common goal: moving the frontiers of health monitoring and probing human health. Our mission is driven by the vision of a reliable, cost-effective approach to safeguard the health of whole populations and develop new ways for the earliest possible detection of diseases such as cancer, cardiovascular disease, and diabetes.
As part of our groundbreaking H4H (Health for Hungary - Hungary for Health) Program, we are building one of the most ambitious longitudinal health monitoring initiatives in Europe. Over a 10-year period, biofluid samples are being collected from thousands of volunteers to enable large-scale molecular profiling and early disease detection.
To support this effort, CMF is establishing a large-scale metabolomics and lipidomics platform for the analysis of human plasma samples. Over the next three years, more than 100,000 plasma samples will be analyzed using a high-throughput LC–MS platform consisting of seven Waters G3 QTOF mass spectrometers.
We are looking for an experienced MS-based omics data processing scientist to lead our raw data preprocessing and quality control workflows through the generation of analysis-ready datasets. This role is ideal for someone with a strong background in LC-MS metabolomics data analysis who enjoys developing robust, automated data processing pipelines for routine sample analysis.