Data Scientist (Quarles) / Cancer Systems Imaging

Summary: The Cancer Systems Imaging lab led by Dr. Chad Quarles seeks a highly motivated Data Scientist to support research in quantitative imaging and computational modeling for cancer detection, diagnosis, and treatment response assessment. The role involves developing algorithms, software tools, and pipelines for analyzing high-dimensional imaging and multi-omics data, impacting translational and clinical research in precision oncology.

Ideal Candidate: Experience in designing, optimizing, and programming pulse sequences and image reconstruction algorithms for clinical MRI systems, with expertise in multi-contrast, quantitative physiologic or metabolic imaging, especially high temporal/spatial resolution imaging.

Job Specific Competencies: - Develop and optimize algorithms for MRI, PET, CT data analysis using signal processing, statistical modeling, and AI/ML. - Analyze and integrate multimodal datasets including imaging, genomics, and clinical data. - Develop and maintain software tools for image analysis workflows. - Apply deep learning and machine learning methods for predictive modeling. - Implement image processing, visualization, and collaborate with clinical teams. - Document processes, contribute to research outputs, and support data management. - Support infrastructure, ensure reproducibility, and engage in professional development.

Requirements: - Education: Bachelor's in Biomedical Engineering, Electrical Engineering, Physics, or related field; Master's or PhD preferred. - Experience: 3 years in scientific software or industry analysis; less with higher degrees. - Skills: Proficiency in Python, R, MATLAB, experience with imaging modalities, deep learning, cloud computing, Docker, Git. - Onsite presence: Hybrid. Additional information about salary, benefits, and legal policies are provided in the original description. This job is active, with no indication of expiration.