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6 days ago
We are seeking a highly motivated PhD candidate interested in machine learning for histopathology image analysis.
The ideal candidate will contribute to developing and optimizing deep learning models to analyze digitized H&E slides for cancer classification and spatial mapping.
- Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images.
- Use and extend tools such as QuPath for cell annotations, segmentation models, and dataset curation.
- 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.
- PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related discipline.
- 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.
- Ability to work collaboratively in a multidisciplinary research environment.
- Background in cancer histopathology or biomedical image analysis.
- Knowledge of multimodal data integration, including spatial transcriptomics.
This is a great opportunity for researchers aiming to apply advanced computational methods to biomedical challenges.
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