Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Johnson & Johnson

Developing AI-driven frameworks for neurodegeneration research at Johnson & Johnson. Collaborating with the Machine Intelligence team on multi-modal neuroscience data integration.

Posted 7/8/2026full-timeCambridge • California, Massachusetts, New Jersey, Pennsylvania • 🇺🇸 United StatesMid-LevelSenior💰 $79,000 - $127,650 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in neurodegenerative disease biology and proficiency in integrating diverse biomedical data modalities to develop AI-driven models. Capable of translating complex evaluation findings into actionable insights while effectively communicating across interdisciplinary teams.

Highest-signal resume keywords

PhD In NeuroscienceNeuroimaging ExperiencePython ProficiencyLarge Language Model FamiliarityNeurodegenerative Disease Knowledge

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Hard Skills

Data EngineeringAI Model EvaluationBiomedical Data IntegrationStatistical AnalysisMachine Learning Frameworks

Soft Skills

Scientific CommunicationSelf-Directed WorkTeam Collaboration

Tools & Technologies

LangGraphDSPyML Frameworks

Industry Keywords

Digital PathologyOmicsLongitudinal Clinical DataNeurodegenerationCausal Reasoning

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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, 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.
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance.
Company sponsored employee benefit programs.
Vacation – up to 120 hours per calendar year.
Sick time - up to 40 hours per calendar year.
Holiday pay, including Floating Holidays – up to 13 days per calendar year.
Work, Personal and Family Time - up to 40 hours per calendar year.