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Job description
Post Doc – Data Analytics & Computational Sciences
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. 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 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
Required Qualifications
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., LangGraph, 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
Self-directed, with the ability to work both independently and within a diverse, multi-disciplinary team
Preferred Qualifications
Experience building data pipelines that integrate heterogeneous biomedical data types
Familiarity with evaluation or benchmarking methodologies for AI/ML systems
Experience with NLP techniques: named entity recognition, natural language inference, knowledge graph construction
Knowledge of graph data structures, graph analytics, and graph platforms (Neo4j, Neptune)
Familiarity with cloud infrastructure (AWS and/or Azure) for scalable pipelines
Worksite address
cambridge, MA, 02140, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.