Postdoctoral Fellow - Multi-modal Omics & Neurodegeneration

The BrainSignatures Lab (BSL) in its current form is a proteomics lab dedicated to finding biofluid signatures of Alzheimer's disease and other dementias. We follow multiple longitudinal cohorts of aging and Alzheimer's disease that have now started yielding important insights into the pathophysiology of Alzheimer's disease using both targeted and unbiased high throughput protein assays. We are in the process of measuring and analyzing high-throughput, multi-dimensional omics data integrating single cell transcriptomics and spatial proteomics of immune cells from CSF and blood as well as human brain tissue from the large brain bank at JHU to develop an integrated understanding of the disease. The ultimate goal of the BSL is to understand the biology of these diseases, to define novel biomarkers, and to identify novel therapeutic targets. The Johns Hopkins University School of Medicine is renowned for its research cohorts of neurodegenerative disorders (JH-ADRC, BIOCARD, BLSA), which include clinical, neuroimaging, fluid biomarker phenotypes and neuropathology for validation.

Your mission :

Extract novel biomarker signatures and therapeutic targets that explain disease heterogeneity across the Alzheimer's spectrum.

You can :

Lead de-novo multiomic integration pipelines.

Design your own sub-project leveraging cutting-edge wet-lab platforms (Olink Explore, DIA_MS, NULISA, Aviti24 cytoprofiling and tissue transcriptomics) available in the lab.

Publish first-author papers and present at ISMB, AAIC, ANA and others

What you will do :

Analyze large-scale proteomic, single-cell, spatial datasets to discover pathway-level disease mechanisms.

Build reproducible pipelines in R / Python Integrate proteomic signatures with neuroimaging, cognitive and neuropathology phenotypes.

Write manuscripts, contribute to grant proposals, and present findings at international conferences.

What you bring :

PhD (or near completion) in Bioinformatics, Computational Biology, Data Science, Statistical Genomics, or related field. Proficiency in R and Python; comfortable on Linux clusters. Bonus points for Julia or cloud-native workflows.

Proven analytical reasoning—able to translate numbers into biological insight. Desire to innovate independently while thriving in a collaborative, multidisciplinary team.

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