Translational Post Doctoral Researcher - Agentic AI Neurodegeneration

Position

Johnson & Johnson Innovative Medicine is seeking a Translational Postdoctoral Researcher — Agentic AI for Neurodegeneration for a 2-year fixed-term position.

Locations:
Raritan, New Jersey;
Titusville, New Jersey;
Spring House, Pennsylvania;
San Diego, California;
Cambridge, Massachusetts. (No remote option.)

The next frontier in neurodegeneration research is integrating insights across the data we already have at scale with agentic AI. Whole slide pathology, PET and MRI imaging, multi‑omics, and longitudinal clinical records each offer a different lens on neurodegenerative diseases. This integration challenge is reshaping how we build agentic AI systems for drug discovery and how we evaluate them.

As a Postdoctoral Researcher in the Machine Intelligence team at J&J Innovative Medicine, you will work with AI scientists and translational teams across C‑BRAIN’s academic network to develop evaluation frameworks that test agentic AI systems across multiple modalities.

• 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

• 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 or failed clinical trials, 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 multimodal datasets that probe AI co‑scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert‑level biomedical reasoning

• 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

• PhD (or MD/PhD) in neuroscience, neurobiology, computational neuroscience, biomedical informatics, or a closely related field. (
  * Degree must have been completed within the last 3 years, or will be completed in the next 6 months.)
• Deep knowledge of neurodegenerative disease biology (Alzheimer’s, Parkinson’s, etc.) including disease mechanisms, experimental models, and translational challenges
• Hands‑on experience working with at least two of the following data modalities in a research context: neuroimaging (PET, MRI), digital pathology, omics, longitudinal clinical data
• Familiarity with large language model architectures and agentic AI frameworks (e.g., Lang Graph, DSPy, or equivalent orchestration tools)
• Proficiency in Python and common ML/data engineering frameworks
• Excellent scientific communication skills and comfort working across computational, translational, and experimental teams
• Se…