Translational Post Doctoral Researcher - Agentic AI for Neurodegeneration

Key Responsibilities

• 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.

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.