AI/ML Scientist, Digital Histopathology

As a H&E Image Analysis Scientist / Machine Learning Engineer specializing in Spatial Omics with a PhD background, you will be involved in developing and optimizing deep learning models for histopathology image analysis. Your main focus will be on analyzing digitized H&E slides for cancer classification and spatial mapping, utilizing advanced computational methods to tackle biomedical challenges. Key Responsibilities: - Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images. - Utilize and enhance tools like QuPath for cell annotations, segmentation models, and dataset curation. - Preprocess, annotate, and manage large image datasets to facilitate model training and validation. - Engage in collaboration with cross-disciplinary 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 findings and assist in generating publications for peer-reviewed journals. Qualifications: - PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related field. - Demonstrated research experience in machine learning, deep learning, or biomedical image analysis, evidenced through publications, thesis projects, or conference presentations. - Proficient programming skills in Python and hands-on experience with ML frameworks such as TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to effectively collaborate in a multidisciplinary research environment. Preferred Qualifications: - Background in cancer histopathology or biomedical image analysis. - Knowledge of multimodal data integration, including spatial transcriptomics. Please feel free to share your resume for further consideration. As a H&E Image Analysis Scientist / Machine Learning Engineer specializing in Spatial Omics with a PhD background, you will be involved in developing and optimizing deep learning models for histopathology image analysis. Your main focus will be on analyzing digitized H&E slides for cancer classification and spatial mapping, utilizing advanced computational methods to tackle biomedical challenges. Key Responsibilities: - Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images. - Utilize and enhance tools like QuPath for cell annotations, segmentation models, and dataset curation. - Preprocess, annotate, and manage large image datasets to facilitate model training and validation. - Engage in collaboration with cross-disciplinary 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 findings and assist in generating publications for peer-reviewed journals. Qualifications: - PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related field. - Demonstrated research experience in machine learning, deep learning, or biomedical image analysis, evidenced through publications, thesis projects, or conference presentations. - Proficient programming skills in Python and hands-on experience with ML frameworks such as TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to effectively collaborate in a multidisciplinary research environment. Preferred Qualifications: - Background in cancer histopathology or biomedical image analysis. - Knowledge of multimodal data integration, including spatial transcriptomics. Please feel free to share your resume for further consideration.