Postdoctoral Scientist, NeuroAI & Multi-Modal Data Analytics

Scorpion Therapeutics; New Jersey; On-site; USD 70,000 - 90,000; Full time

Job summary

Johnson & Johnson's Innovative Medicine division seeks a Postdoctoral researcher in Data Analytics & Computational Sciences focused on agentic AI for neurodegeneration. You will develop multi-modal data integration and evaluation frameworks synthesizing pathology, neuroimaging, omics, and clinical data to advance neurodegenerative understanding.

Responsibilities include harmonizing data modalities, designing AI benchmarks, translating findings into ML applications, and publishing methodologies

Qualifications

• PhD (or MD/PhD) in neuroscience, neurobiology, biomedical informatics or related field.
• Hands-on experience with neuroimaging, digital pathology, omics, or clinical data.
• Proficiency in Python and ML frameworks; familiarity with agentic AI architectures and LLMs.
• Strong communication skills and ability to work independently.

Responsibilities

• Characterize and harmonize diverse biomedical data modalities.
• Design benchmarks and standards for evaluating AI co-scientists across modalities.
• Translate findings into practical AI/ML applications.
• Publish methodologies and findings; collaborate with academic and internal teams.

Skills

Python

ML frameworks

Neuroimaging

Digital pathology

Omics

Clinical data

NLP techniques

Graph analytics

Self-directed

Publication potential

Education

PhD (or MD/PhD) in neuroscience, neurobiology, biomedical informatics or related field

Tools

Large language models (LLMs)

Cloud platforms

Benchmarking frameworks

Data integration pipelines

Job description

Johnson & Johnson's Innovative Medicine division seeks a Postdoctoral researcher in Data Analytics & Computational Sciences focused on agentic AI for neurodegeneration. You will develop multi-modal data integration and evaluation frameworks synthesizing pathology, neuroimaging, omics, and clinical data to advance neurodegenerative understanding.

Responsibilities include harmonizing data modalities, designing AI benchmarks, translating findings into ML applications, and publishing methodologies