Postdoctoral Fellow – Computational Biology / Bioinformatics (Alzheimer’s)

About the role

We are seeking a highly motivated Postdoctoral Fellow with a PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, or a related quantitative field to join an interdisciplinary research program focused on Alzheimer’s disease and neurodegeneration at Saint Louis University.

Key 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 such as mixed‑effects models, survival analysis, dimensionality reduction, clustering, and trajectory modeling.
• Lead reproducible analysis pipelines using R, Python, or related frameworks.
• Interpret results in a biological and clinical context, emphasizing 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.
• Mentor graduate or undergraduate trainees in computational methods (optional).

Required profile

• 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 such as peer‑reviewed publications, preprints, or advanced projects.

Required skills

• R programming
• Python programming
• Statistical modeling (mixed‑effects, survival analysis)
• Machine‑learning techniques
• Dimensionality reduction, clustering, trajectory modeling
• Multi‑omics integration (proteomics, transcriptomics, imaging)

What we offer

• Full‑time, 1‑year fixed‑term appointment (renewable pending funding and performance).
• Opportunity to contribute to high‑impact publications and grant development.