Designing and implementing a cutting-edge foundation model that integrates high-dimensional medical imaging data (CT, MRI) with unstructured radiological text reports., Developing and implementing robust pipelines for the curation, integration, and preprocessing of heterogeneous data from diverse sources, including clinical PACS systems and large-scale research cohorts (e.g., NAKO, UK Biobank, TCIA)., Implementing and refining innovative learning strategies, including contrastive learning (anatomical, multi-view, pathological contrasts) and weakly supervised approaches, to train a model for robust feature extraction and generalizability., Enabling the extraction of generalizable, image-based biomarkers from the learned representations, aiming to improve clinical decision-making in oncology., Developing and optimizing data loading and training infrastructure (PyTorch, MONAI) for efficient handling of large 3D medical datasets and high-performance GPU clusters, including modality-specific data augmentation and adaptive sampling algorithms., Working within a dynamic, interdisciplinary team of clinicians and AI researchers, and disseminate your findings through high-impact publications and conference presentations.
