Translational Post Doctoral Researcher - Agentic AI Neurodegeneration
Listed on 2026-07-19
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Research/Development
Data Scientist, Research Scientist
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Job FunctionCareer Programs
Job Sub FunctionPost Doc – Data Analytics & Computational Sciences
Job CategoryCareer Program
LocationsCambridge, Massachusetts;
Raritan, New Jersey;
San Diego, California;
Spring House, Pennsylvania;
Titusville, New Jersey
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.
Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Johnson & Johnson Innovative Medicine is seeking a Translational Post Doctoral Researcher — Agentic AI for Neurodegeneration for a 2-year fixed term position. This position can be located in either Raritan NJ, Titusville NJ, Spring House PA, San Diego CA or Cambridge MA. (No fully remote option.)
The next frontier in neurodegeneration research is integrating insights across the data we already have at scale with agentic AI in ways that were previously not possible. Whole slide pathology, PET and MRI imaging, multi-omics, and longitudinal clinical records each offer a different lens on the neurodegenerative diseases; brought together, they tell a story no single modality can. This integration challenge is reshaping how we build agentic AI systems for drug discovery and how we evaluate them.
Traditional benchmarks were composed for single-modality reasoning. Evaluating whether an AI co-scientist can synthesize across pathology, imaging, molecular, and clinical evidence and produce hypotheses that are biologically sound, demands new frameworks. We are seeking a Postdoctoral Researcher to build them.
The Researcher will be embedded in the Machine Intelligence (MI) team at J&J Innovative Medicine, working in partnership with the c-brAIn academic network. The role begins with engagement in multi-modal neuroscience data — understanding what each modality reveals, how they relate, and where integration breaks down — and builds toward crafting the evaluation frameworks and standards by which agentic co‑scientist systems are tested, validated, and trusted.
The Researcher will work day‑to‑day with AI scientists in J&J’s Machine Intelligence group while partnering closely with translational and experimental teams across C‑BRAIN’s academic network at Washington University in St. Louis and partner institutions. Mentorship is designed to build leaders at the Multi‑Modal Data × AI Evaluation × Neurodegeneration interface, with opportunities for publications, cross‑sector exposure, and leadership development.
Key Responsibilities Multi‑Modal Data Integration- Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain‑specific models for neurodegeneration
- Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks
- Identify where cross‑modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI…
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