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2 days ago
We are seeking a motivated PhD candidate interested in developing and optimizing deep learning models for histopathology image analysis. The candidate will contribute to creating novel algorithms and techniques to analyze digitized H&E slides for cancer classification and spatial mapping.
Key Responsibilities:
- Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images.
- Preprocess, annotate, and manage large image datasets to support model training and validation.
- Collaborate with cross-disciplinary teams to integrate image-based predictions with molecular and clinical data.
- Analyze model performance and contribute to improving accuracy, efficiency, and robustness.
Necessary Skills:
- 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.
- Strong programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch.
- Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools.
Preferred Qualifications:
- Background in cancer histopathology or biomedical image analysis.
- Knowledge of multimodal data integration, including spatial transcriptomics.
About the Opportunity:
This is an excellent opportunity for a talented scientist to make a meaningful contribution to the development of cutting-edge technologies for cancer diagnosis and treatment.
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