Neuroimaging Data Scientist - MRI/PET & AI Pipelines

CHDI Management; Princeton (NJ); Hybrid; USD 115,000 - 145,000; Full time

Benefits offered by this job

COVID-19 vaccination prerequisite

Job summary

CHDI Foundation is expanding its neuroimaging analytics program to accelerate biomarker discovery for Huntington’s disease. We seek a scientist with MRI and/or PET data analytics, quantitative image processing, and applied deep learning to design and implement data pipelines and AI methods.

The role involves overseeing data handling across imaging modalities, building reproducible pipelines, and deploying AI-driven approaches for biomarker discovery and multi-site harmonization.

Qualifications

• MS or BA in a quantitative field and 3+ years of medical imaging data analysis.
• Proficiency in Python, MATLAB, or R and AI/ML frameworks (PyTorch, TensorFlow).
• Experience with neuroimaging packages (FreeSurfer, FSL, SPM, ANTs, Nipype) and data standards (DICOM, BIDS).

Responsibilities

• Develop robust data pipelines for MRI and PET data ingestion, curation, de-identification, BIDS conversion, and QC across sites.
• Develop algorithms for brain segmentation, tissue classification, harmonization, denoising, and anomaly detection.
• Handle dynamic imaging data and 4D data analysis.
• Model PET tracer quantification (SUVR, DVR, binding potential) and dynamic data.
• Apply ML/DL for biomarker extraction and predictive modeling.
• Cross-modality data fusion and multi-site harmonization.
• Prepare technical reports and visualizations for reviews and collaborations.

Skills

MRI analytics

PET analytics

Quantitative image processing

Deep learning

Python

MATLAB

R

AI/ML frameworks

Education

MS or BA in a quantitative field

Tools

FreeSurfer

FSL

SPM

ANTs

PMOD

Nipype

DICOM

BIDS

PyTorch

TensorFlow

Job description

CHDI Foundation is expanding its neuroimaging analytics program to accelerate biomarker discovery for Huntington’s disease. We seek a scientist with MRI and/or PET data analytics, quantitative image processing, and applied deep learning to design and implement data pipelines and AI methods.

The role involves overseeing data handling across imaging modalities, building reproducible pipelines, and deploying AI-driven approaches for biomarker discovery and multi-site harmonization.