Computational Scientist
Listed on 2026-06-18
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Research/Development
Research Scientist, Data Scientist, Biomedical Science, Biotechnology
The Stowers Institute for Medical Research seeks an accomplished computational scientist to serve as Lead of Computational Mass Spectrometry (MS) and Innovation, within our Systems Mass Spectrometry (SMS) Technology Center. The leadership role sits at the intersection of innovative technology, scientific collaboration, and the Institute’s mission to advance our understanding of life’s fundamental processes. The successful candidate will help drive a cutting‑edge core facility at the heart of a vibrant, multidisciplinary research community, and is expected to bring a strong track record in mass spectrometry data analysis, reporting, and methodological innovation, together with exemplary communication, collaboration, and leadership skills.
Overviewof the Role
Biological mass spectrometry is entering a transformative era defined by AI-enabled analysis, increasing data scale, and proteoform-level resolution.
This role offers a rare opportunity to shape the analytical foundations of next-generation mass spectrometry-based multiomics (proteomics, metabolomics, lipidomics) and to define how advanced computation and AI unlock new biological and biomedical insights. The Lead of Computational MS and Innovation will be empowered to build new capabilities, pursue bold ideas, and influence the direction of biological mass spectrometry research at an institutional level.
Reporting to the Director of Systems Mass Spectrometry, the scientist will lead cutting-edge analysis of data generated by a broad portfolio of modern MS methods, including bottom-up, top-down, native, cross-linking and spatial mass spectrometry, as well as multiomics (metabolomics and lipidomics). The successful candidate will also contribute to project design, and technology development while serving as a scientific and technical resource for the Institute’s investigators.
The position requires deep technical expertise, collaborative spirit, and outstanding interpersonal skills, with regular interaction across more than 20 independent research programs and a spectrum of technology development facilities. Their lead will also help establish standard operating protocols for results reporting and will champion cross-technology collaboration that merges new‑generation mass spectrometry methods with biological discovery.
- Define and execute a long-term computational proteomics and AI innovation strategy aligned with institutional research priorities.
- Serve as the intellectual leader for computational analysis of large-scale proteomics, native and top‑down proteomics, PTM analysis, and integrative multi‑omics datasets.
- Identify emerging technologies, analytical paradigms, and AI methodologies that can transform proteomics data interpretation and biological insight.
- Partner with computational scientists in other technology centers and PI laboratories to integrate mass spectrometry data with genomics, transcriptomics, and microscopy datasets.
- Drive high‑impact publications, presentations, and dissemination of novel computational methods.
Lead the development and deployment of machine learning and AI approaches for proteomics, including:
- Deep learning for peptide and proteoform identification and scoring
- AI-based spectral prediction and library‑free analysis
- Methods for both DIA and DDA acquisition strategies
- Automated proteoform annotation and confidence assessment
- Explore and implement generative AI, foundation models, and representation‑learning approaches for proteomics and multi‑omics data.
- Drive innovation in scalable, automated, and reproducible analysis pipelines for high‑throughput proteomics.
- Oversee the design, maintenance, and evolution of computational pipelines for proteomics data processing, quality control, statistical analysis, and visualization.
- Guide the integration of proteomics data with genomics, transcriptomics, and metabolomics datasets.
- Partner with IT and the Big Data team at Stowers to ensure robust data management, cloud/HPC utilization, and FAIR data practices.
- Work closely with experimental proteomics…
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