The qualifications are (1) PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a closely related field; (2) strong background in deep learning and medical image analysis, with demonstrated experience in image segmentation, classification, detection, or multimodal learning for radiology applications; (3) strong publication record as the first author in peer-reviewed venues such as MICCAI, ISBI, CVPR, IEEE Transactions on Medical Imaging, Medical Image Analysis, Radiology journals, or equivalent venues; (4) Experience with multiparametric MRI analysis and/or radiology-pathology correlation analysis in prostate cancer is a plus; (5) Experience with clinical NLP applied to clinical or radiology text (e.g., report generation, entity extraction, or LLM fine-tuning) is a plus; (6) Familiarity with agentic AI frameworks, AI infrastructure, and deployment tools (e.g., high-throughput LLM inference frameworks vLLM) is a plus. The key responsibilities include: (1) design and develop multi-modal AI models for registration, detection, segmentation, and grading tasks using multi-parametric MRI and histopathology data for prostate cancer; (2) collaborate closely with radiologists and clinical specialists to formulate clinically relevant research questions and develop AI-driven solutions to improve prostate cancer patient care; (3) contribute to the preparation and submission of scientific manuscripts; (4) contribute to grant applications to federal or industry-sponsored research; (5) Help mentor graduate students on project-specific tasks.