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vor 3 Wochen
**Title: Building Trustworthy AI Models for Uncertainty Estimation in Medical Imaging**
In J&J Computer vision team, we focus on developing AI/ML solutions for clinical trials and drug discovery. The goal of this master's thesis project is to investigate and develop methods for uncertainty estimation in deep learning models applied to medical imaging. The focus will be on enhancing the trustworthiness of AI models by providing accurate uncertainty estimates, a crucial aspect for their deployment in medical settings.
**Tasks**:
- Conduct a literature review to understand existing uncertainty estimation approaches in medical imaging.
- Implement uncertainty estimation methods using PyTorch, integrating them into medical image analysis architectures.
- Design and conduct experiments to evaluate method performance on diverse medical imaging datasets.
- Integrate uncertainty estimation methods into existing AI models, assessing their impact on performance and interpretability on real-world datasets.
- Collaborate with stakeholders to gather feedback and validate real-world effectiveness.
**Qualifications**:
- Must be student at ETH Zurich, currently pursuing MSc.
- Proficiency in Python and PyTorch.
- Strong theoretical and practical understanding of computer vision and deep learning.
- Knowledge of biomedical imaging, high-content imaging, or multiplexed imaging is nice to have, but not necessary.
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