p>• 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). • 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).