AI Scientist, Histopathology Imaging

As an H&E Image Analysis Scientist / Machine Learning Engineer specializing in Spatial Omics, your main role will involve developing and optimizing deep learning models for the analysis of digitized H&E slides focused on cancer classification and spatial mapping. This position offers an exciting opportunity for researchers passionate about applying advanced computational techniques to biomedical challenges. **Key Responsibilities:** - Design, develop, and train convolutional neural networks (CNNs) and related ML models specifically for H&E-stained histology images. - Utilize and expand tools like QuPath for cell annotations, segmentation models, and dataset curation. - Preprocess, annotate, and manage large image datasets to facilitate model training and validation. - Collaborate with diverse teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and actively contribute to enhancing accuracy, efficiency, and robustness. - Document research outcomes and participate in publishing results in reputable journals. **Qualifications:** - Possession of a PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related field. - Demonstrated research background in machine learning, deep learning, or biomedical image analysis through publications, thesis projects, or conference presentations. - Proficient programming skills in Python and hands-on experience with ML frameworks like TensorFlow or PyTorch. - Knowledge of digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to collaborate effectively within a multidisciplinary research environment. **Preferred Skills:** - Previous experience in cancer histopathology or biomedical image analysis. - Understanding of multimodal data integration, including spatial transcriptomics. In addition to the above job-specific details, please share your resume for further consideration. As an H&E Image Analysis Scientist / Machine Learning Engineer specializing in Spatial Omics, your main role will involve developing and optimizing deep learning models for the analysis of digitized H&E slides focused on cancer classification and spatial mapping. This position offers an exciting opportunity for researchers passionate about applying advanced computational techniques to biomedical challenges. **Key Responsibilities:** - Design, develop, and train convolutional neural networks (CNNs) and related ML models specifically for H&E-stained histology images. - Utilize and expand tools like QuPath for cell annotations, segmentation models, and dataset curation. - Preprocess, annotate, and manage large image datasets to facilitate model training and validation. - Collaborate with diverse teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and actively contribute to enhancing accuracy, efficiency, and robustness. - Document research outcomes and participate in publishing results in reputable journals. **Qualifications:** - Possession of a PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related field. - Demonstrated research background in machine learning, deep learning, or biomedical image analysis through publications, thesis projects, or conference presentations. - Proficient programming skills in Python and hands-on experience with ML frameworks like TensorFlow or PyTorch. - Knowledge of digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to collaborate effectively within a multidisciplinary research environment. **Preferred Skills:** - Previous experience in cancer histopathology or biomedical image analysis. - Understanding of multimodal data integration, including spatial transcriptomics. In addition to the above job-specific details, please share your resume for further consideration.