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.

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

Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence

Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning

Research & Communication

Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions

Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives

Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS)

Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery

Co-author manuscripts, concept papers, and translational strategy documents