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Assistant Professor / Computational Scientist
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- As part of an interdisciplinary team, ML scientist will work on developing advanced image analysis techniques (segmentation, classification, object detection, quantification) to support precision medicine applications.
- The successful candidate will work collaboratively with a diverse team of researchers, pathologists, and clinicians; develop best-in-class algorithms that directly address important biological and clinical questions, and incorporate image-based data with clinical and molecular data to drive translational and clinical research projects including in the field of immuno-oncology.
- Advanced degree (Ph. D. or M.D./Ph. D.) in computer science, biomedical engineering, biomedical imaging or a related field
- Experience developing, training, and evaluating classical machine/deep learning models, such as SVMs, Random Forests, decision trees, image segmentation (U-NET), object detection autoencoders, Gradient Boosting, CNN, FCN, ResNet, GAN, clustering, PCA and etc.
- Experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, C
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