AI/ML Scientist, Digital Histopathology

You are a motivated PhD candidate interested in machine learning for histopathology image analysis. Your main role will involve developing and optimizing deep learning models to analyze digitized H&E slides for cancer classification and spatial mapping. This position is ideal for researchers who want to apply advanced computational methods to biomedical challenges. **Key Responsibilities:** - Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images. - Utilize 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 cross-disciplinary teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and contribute to enhancing accuracy, efficiency, and robustness. - Document research findings and participate in publications in peer-reviewed journals. **Qualifications:** - PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or related field. - Demonstrated research experience in machine learning, deep learning, or biomedical image analysis (e.g., publications, thesis projects, or conference presentations). - Proficient in Python programming and experienced with ML frameworks like TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Capable of working collaboratively in a multidisciplinary research environment. *Preferred:* - Background in cancer histopathology or biomedical image analysis. - Knowledge of multimodal data integration, including spatial transcriptomics. (Note: The job description did not include any additional details about the company.) You are a motivated PhD candidate interested in machine learning for histopathology image analysis. Your main role will involve developing and optimizing deep learning models to analyze digitized H&E slides for cancer classification and spatial mapping. This position is ideal for researchers who want to apply advanced computational methods to biomedical challenges. **Key Responsibilities:** - Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images. - Utilize 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 cross-disciplinary teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and contribute to enhancing accuracy, efficiency, and robustness. - Document research findings and participate in publications in peer-reviewed journals. **Qualifications:** - PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or related field. - Demonstrated research experience in machine learning, deep learning, or biomedical image analysis (e.g., publications, thesis projects, or conference presentations). - Proficient in Python programming and experienced with ML frameworks like TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Capable of working collaboratively in a multidisciplinary research environment. *Preferred:* - Background in cancer histopathology or biomedical image analysis. - Knowledge of multimodal data integration, including spatial transcriptomics. (Note: The job description did not include any additional details about the company.)