Data Scientist - Medical Imaging Ai, Emory University

As a Data Scientist, you will be a key contributor to our cutting-edge research initiatives in Cancer Imaging. You will play a leading role in developing multimodal foundation models for downstream tasks such as classification, segmentation, and synthetic image/text generation. Starting with mammography, the successful candidate will work with diverse imaging modalities, including Chest X-rays, CT, and MRI, to address specific challenges in each domain.
Responsibilities:

Model Development:
Design generalist pre-trained models for Medical Imaging using strategies like transfer learning, contrastive training, masking and self-supervised modeling. Implement and experiment with architectures such as CNNs, EfficientNet, and Transformers.
Multimodal Understanding:
Develop large language models and large vision models to investigate representation learning, visual question answering and visual Focus on learning joint representations from imaging data and clinical reports for comprehensive
Data Preprocessing:
Prepare and preprocess medical images using Python and relevant libraries (e.g., pandas, NumPy, OpenCV). Handle data augmentation, normalization, and other preprocessing steps to enhance model performance. Maintain clear and comprehensive documentation for models, datasets, and preprocessing
Collaboration:
Collaborate with domain experts to ensure accurate and meaningful labeled datasets for training and evaluation. Participate in discussions to understand clinical requirements and adapt models accordingly. Collaborate with radiologists, Data Scientists, and software engineers to align research objectives with real-world Medical Imaging needs.

Requirements:

Advanced degree in computer science, machine learning, or a related field. Experience in applying deep learning techniques to Medical Imaging modalities (Chest X-rays, CT, MRI). Proficiency in Python and relevant libraries (TensorFlow, PyTorch, scikit-learn, pandas). Strong understanding of image preprocessing techniques and data augmentation. Experience with pre-training techniques (contrastive models, CNNs, and transformer architectures). Familiarity with Medical Imaging standards and protocols.

Preferred Qualifications:

PhD in Machine Learning, Computer Science, or a related field with a focus on Health. Knowledge of healthcare regulations and privacy considerations. Experience working on collaborative projects with industry partners and healthcare institutions. Published research or contributions to the field of medical image analysis.