Postdoc: Interpretable AI for Radiation Cell Biology

Argonne National Laboratory; Lemont (IL); On-site; USD 73,000 - 121,000; Full time

Job summary

Argonne National Laboratory's Data Science Learning division seeks a postdoctoral researcher to develop AI and computational biology methods for studying the effects of low-dose ionizing radiation on cells. The role integrates experimental biology, machine learning, and mechanistic modeling within the LUCID program.

The candidate will analyze large-scale cellular imaging and omics data to identify radiation-induced states, build predictive models, and contribute to translational insights, with

Qualifications

• PhD completed or soon to be completed with 0+ years of experience
• Experience applying AI and computational biology methods
• Ability to analyze and integrate large-scale cellular imaging, transcriptomic, and genomic datasets
• Experience with multimodal data integration and interpretable AI

Responsibilities

• Multimodal data integration across imaging, transcriptomics, and other molecular measurements.
• Develop and evaluate ML models including representation learning and foundation-model approaches.
• Analyze time-resolved datasets to study cellular adaptation and DNA damage responses.
• Apply explainable AI methods to identify molecular signatures and pathways.
• Assess model robustness and predictive performance across experiments and cell types.
• Collaborate with experimental biologists to formulate testable hypotheses and evaluate predictions.

Skills

AI methods

Computational biology

Multimodal learning

Interpretability

Data integration

Education

PhD in a relevant field

Tools

Cell Painting

RNA sequencing

Job description

Argonne National Laboratory's Data Science Learning division seeks a postdoctoral researcher to develop AI and computational biology methods for studying the effects of low-dose ionizing radiation on cells. The role integrates experimental biology, machine learning, and mechanistic modeling within the LUCID program.

The candidate will analyze large-scale cellular imaging and omics data to identify radiation-induced states, build predictive models, and contribute to translational insights, with