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We are seeking a motivated MSc student to join our R&D team as a Machine Learning Engineer focusing on medical image processing (CT, MRI). In this role, you will support the development and optimization of deep learning models for image segmentation and analysis, leveraging U-Net and 3D U-Net architectures.
This is a hands-on opportunity to work on real-world clinical data and contribute to next-generation solutions for diagnostic and procedural planning workflows.
How you will make an impact:
Model Development:
Assist in designing and implementing deep learning models for medical image segmentation using U-Net, 3D U-Net, and nnU-Net frameworks.
Support optimization of models for volumetric CT/MRI datasets.
Data Handling & Processing:
Develop preprocessing pipelines (normalization, augmentation, resampling).
Integration & Experimentation:
Support integration of models into research pipelines using tools like MONAI and 3D Slicer.
Run experiments, evaluate model performance, and document results.
Collaboration:
Work closely with engineers, data scientists, and clinical teams.
Participate in technical discussions and contribute ideas for improving model performance.
Research & Innovation:
Stay updated on state-of-the-art methods in medical imaging AI and segmentation.
Explore improvements in model architecture and training strategies.