We are seeking a Sr. Radiomics and AI Engineer who will be responsible for developing and implementing algorithms and software that can analyze medical images and extract clinically relevant features using machine learning and deep learning techniques for the Dept. of Radiology’s Diagnostic Imaging Division. Specific Duties & Responsibilities * Develop and implement machine learning and deep learning algorithms to analyze medical images. * Collaborate with radiologists, oncologists, and other healthcare professionals to identify clinically relevant features that can be extracted from medical images. * Design and implement data preprocessing pipelines to ensure high-quality input data for the algorithms. * Develop and maintain software tools for data analysis, visualization, and reporting. * Test and validate the algorithms and software using clinical data sets and real-world scenarios. * Continuously monitor and improve the performance of the algorithms and software. * Stay up to date with the latest research and developments in radiomics, medical imaging, and artificial intelligence. * Maintain the cluster, computational, and storage resources in the Felix lab, which are used for training and deploying AI models. Minimum Qualifications * Master's in Computer Science, Biomedical Engineering, or a related field. * Two years of experience with machine learning, deep learning, and data analysis. * Additional education may substitute for required experience and additional related experience may substitute for required education beyond HS Diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula. * * * Classified Title: Sr. Radiomics & AI Research Engineer Job Posting Title (Working Title): Sr. Radiomics and AI Engineer (Radiology) Role/Level/Range: ACRP/04/MH Starting Salary Range: $104,035 - $182,245 Annually ($127,000 targeted; Commensurate w/exp.) Employee group: Full Time Schedule: M-F 8:30a-5p FLSA Status: Exempt Location: School of Medicine Campus Department name: SOM Rad Diagnostic Imaging Personnel area: School of Medicine