Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Johnson & Johnson

Translational Post Doctoral Researcher focusing on AI integration in neurodegeneration. Working with multi-modal data to develop AI systems at Johnson & Johnson.

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, including neuroimaging and omics, to develop AI-driven models. Capable of translating complex evaluation findings into actionable insights for AI system development in a collaborative, multi-disciplinary environment.

Highest-signal resume keywords

PhD In NeuroscienceNeurodegenerative Disease BiologyData Engineering PipelinesPython ProficiencyLarge Language Model Architectures

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

NeuroimagingDigital PathologyOmicsLongitudinal Clinical DataAI Evaluation FrameworksCausal ReasoningAgentic PlanningMachine LearningData IntegrationBiomedical Reasoning

Soft Skills

Scientific CommunicationSelf-Directed WorkTeam CollaborationInterdisciplinary Communication

Tools & Technologies

LangGraphDSPyAI/ML FrameworksAnalytical Frameworks

Industry Keywords

NeurodegenerationClinical TrialsTranslational ChallengesResearch ContextEvaluation Methodologies

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.
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
group legal insurance
consolidated retirement plan (pension)
savings plan (401(k))
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 of work, personal and family time - up to 40 hours per calendar year