Research Assistant - School of Medicine, Biomedical Engineering

Emory • Atlanta, GA

7d

About The Position

The primary goal is to explore the usage of machine learning (ML) techniques to analyze large-scale MRI datasets for the extraction of quantitative imaging biomarkers that can predict disease outcomes and improve cardiovascular risk stratification. For example, by using whole-body MRI (wbMRI) to assess organ-specific biological aging, we aim to create standardized and explainable ML models that support early detection of cardiovascular risk. Developing models that perform comparably to state-of-the-art deep learning architectures while improving interpretability and clinical applicability remains a key objective of this analysis. In the next phase, this work will evolve from single-organ analysis to a comprehensive, system-wide understanding of biological aging using the entire 80,000-participant UK Biobank dataset. By developing individualized organ-age models, quantifying systemic aging differences, and standardizing segmentation pipelines, the project will establish a scalable framework for integrating imaging, clinical, and genomic data. These advancements will not only enable high-precision cardiovascular risk prediction but also contribute to a broader understanding of how aging manifests across organ systems. Ultimately, the insights gained will form the foundation for a publishable journal manuscript, support thesis preparation, and advance the long-term goal of translating AI-driven imaging biomarkers into real-world clinical decision-making.

Requirements

• Learning new deep learning-based computer vision algorithms applied to medical image segmentation, focusing on UKBioBank Whole Body images initially.
• Gaining experience in research writing journals and conference publications.
• Developing skills to present research results to the scientific community effectively.

Responsibilities

• Develop ML-based segmentation and biomarker extraction pipelines using large-scale MRI datasets.
• Validate and optimize ML models for robust feature extraction and quantitative model verification, emphasizing explainability and reproducibility.
• Conduct statistical and survival analyses to assess associations between imaging biomarkers and disease outcomes and report the findings to the scientific community.
• Design and implement automated pipelines for MRI processing, segmentation, and biomarker quantification.
• Train, optimize, and validate ML models to ensure robustness and reproducibility while reducing manual analysis time by approximately 70%.
• Compile results and submit a research abstract to a major cardiovascular conference.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed