Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration

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 MedTech, 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. Learn more at jnj.com.

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Job Function:

Career Programs

Job Sub Function:

Post Doc – Data Analytics & Computational Sciences

Job Category:

Career Program

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, Raritan, New Jersey, United States of America, San Diego, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

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.

Learn more at https://www.jnj.com/innovative-medicine

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 which 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 RESPSONSIBILITIES

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 evaluation

Agentic AI Evaluation

• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses

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