Full-Time

Postdoctoral Fellow Computational Biology / Bioinformatics

St. Louis University

St. Louis University

No salary listed

No H1B Sponsorship

St. Louis, MO, USA

In Person

Category
Lab & Research (2)
,
Required Skills
Python
R
Machine Learning
biostatistics
Data Analysis
Requirements
  • PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, Systems Biology, or a related quantitative discipline
  • Strong experience with high-dimensional biological data analysis
  • Proficiency in R and/or Python for statistical computing and data analysis
  • Solid foundation in statistics and data modeling, particularly for longitudinal or cohort-based data
  • Demonstrated ability to work independently and manage complex datasets
  • Strong written and verbal communication skills in English
  • Evidence of productivity (e.g., peer-reviewed publications, preprints, or advanced projects)
Responsibilities
  • Perform computational analysis of large-scale omics datasets, including proteomics, transcriptomics, and related modalities
  • Integrate multi-omics data with clinical, cognitive, and imaging phenotypes in longitudinal cohorts
  • Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival analysis, dimensionality reduction, clustering, trajectory modeling)
  • Lead reproducible analysis pipelines in R, Python, or related frameworks
  • Interpret results in biological and clinical context, with emphasis on Alzheimer’s disease mechanisms and biomarkers
  • Prepare figures, tables, and methods for peer-reviewed manuscripts and conference presentations
  • Collaborate with clinicians, wet-lab scientists, and biostatisticians in an interdisciplinary environment
  • Contribute to grant proposals and progress reports as appropriate
  • Mentor graduate or undergraduate trainees in computational methods (optional, depending on interest)
Desired Qualifications
  • Experience with longitudinal modeling (e.g., mixed-effects models, disease progression modeling)
  • Familiarity with neurodegenerative disease research, Alzheimer’s disease, or aging biology
  • Experience with proteomics platforms (e.g., Olink, SomaScan, mass spectrometry)
  • Knowledge of multi-omics integration, network analysis, or pathway enrichment methods
  • Experience working with large consortium datasets (e.g., ADNI, AMP-AD, UK Biobank, similar)
  • Interest in translational research, biomarker discovery, or drug target identification
  • Experience with reproducible research practices (version control, documentation, workflow tools)

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