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About the role
Role Overview: You will be a part of a dynamic team as a H&E Image Analysis Scientist / Machine Learning Engineer with a focus on Spatial Omics. Your main responsibility will involve utilizing machine learning techniques for histopathology image analysis, specifically in developing and optimizing deep learning models for the analysis of digitized H&E slides to aid in cancer classification and spatial mapping. This role is ideal for individuals who are passionate about applying advanced computational methods to address biomedical challenges. Key Responsibilities: - Design, develop, and train convolutional neural networks (CNNs) and related machine learning models using 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 interdisciplinary teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and contribute to enhancing accuracy, efficiency, and robustness. - Document research outcomes and participate in the publication process in peer-reviewed journals. Qualifications: - 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 in Python programming and experienced with machine learning frameworks like TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to work effectively in a collaborative, multidisciplinary research setting. Additional Company Details: The company prefers candidates with a background in cancer histopathology or biomedical image analysis and knowledge of multimodal data integration, including spatial transcriptomics. Please share your resume with the provided email address. Role Overview: You will be a part of a dynamic team as a H&E Image Analysis Scientist / Machine Learning Engineer with a focus on Spatial Omics. Your main responsibility will involve utilizing machine learning techniques for histopathology image analysis, specifically in developing and optimizing deep learning models for the analysis of digitized H&E slides to aid in cancer classification and spatial mapping. This role is ideal for individuals who are passionate about applying advanced computational methods to address biomedical challenges. Key Responsibilities: - Design, develop, and train convolutional neural networks (CNNs) and related machine learning models using 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 interdisciplinary teams to merge image-based predictions with molecular and clinical data. - Evaluate model performance and contribute to enhancing accuracy, efficiency, and robustness. - Document research outcomes and participate in the publication process in peer-reviewed journals. Qualifications: - 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 in Python programming and experienced with machine learning frameworks like TensorFlow or PyTorch. - Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. - Ability to work effectively in a collaborative, multidisciplinary research setting. Additional Company Details: The company prefers candidates with a background in cancer histopathology or biomedical image analysis and knowledge of multimodal data integration, including spatial transcriptomics. Please share your resume with the provided email address.