Post Doctoral Fellow

Saint Louis University • Saint Louis, MO

9h

About The Position

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 (AD) and neurodegeneration. The fellow will lead and contribute to advanced bioinformatics, multi-omics integration, and statistical modeling efforts using large, well-phenotyped longitudinal datasets (e.g., proteomics, transcriptomics, imaging, clinical, and biomarker data). The position is ideal for a candidate interested in mechanistic discovery, biomarker development, and translational neuroscience, with opportunities for high-impact publications and grant development.

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)

Nice To Haves

• 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)

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)

Benefits

• Competitive salary and benefits commensurate with experience and institutional guidelines

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree