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🎯 Qualifications

• 2+ years of research experience in machine learning or deep learning(in academia or industry), preferably focusing on computer vision, medical image analysis, or natural language processing.
• Masters in computer science, biomedical engineering, electrical engineering, or a related field(or expecting to graduate).
• Proficiency with contemporary machine learning and image analysis techniques, and related software packages (e.g., deep neural nets, PyTorch, Tensorflow).
• Familiarity with the AI model development cycle(including data collection, annotation, hyperparameter tuning, evaluation, deployment).
• Strong interest in working on medical problems and advancing the standard of care using AI.
• Excellent written and verbal communication skills in English.
• Highly responsible, have an eye for detail, and motivated to build high-quality and reliable solutions following the current best practices
• Team player with experience working in interdisciplinary research teams.

🏅 Preferred Experiences

• 3+ years of research experience and 1+ years of industrial research experience(e.g., PhD + 1 year of industry experience or Masters + 1 year of industry experience).
• Solid experience with Git, Python, unit/integration testing, multithreading, and other software development practices.
• Experience working with image data, in particular medical images.
• First-author publications in top-tier Computer Vision or Medical Imaging conferences or journals (CVPR/ICCV/ECCV, NeurIPS/ICLR/MedIA/T-MI/MICCAI), or top-tier clinical journals (e.g., Nature Medicine, JAMA, Lancet Digital Health).
• Contributions to open-source repositories in Computer Vision, Machine Learning, or related areas.
• Participation in competitive Computer Vision/Medical Imaging challenges.
• Passion for advancing healthcare and combating cancer through AI research.
• Proactive in sharing knowledge, initiating collaborations, and promoting a positive research environment.
• Passion for high-quality programming and software engineering to produce and maintain reliable code for the training and evaluation of models.