Intermediate Applied Machine Learning Scientist – Cancer Care AI Center of Excellence (COE) Siemens Healthineers
Tasks
- Analyze dose volume histogram features
- Build preprocessing and dataset curation pipelines
- Collaborate with clinical teams to translate requirements into production ready solutions
- Conduct multi site validation for performance and safety
- Contribute to publications and patents
- Develop and optimize 3D deep learning models for auto segmentation
- Develop treatment response prediction models
- Implement domain adaptation and transfer learning for medical imaging
- Perform feature extraction and landmark detection
- Validate models using Dice Similarity Coefficient and AUROC
Perks/Benefits
- Company holidays
- Defined contribution pension plan
- Flexible benefits plan
- Paid time off
- Parental leave
- Registered retirement savings plan matching
- Sick leave
Skills/Tech-stack
3D Medical Image Segmentation | AUROC | CBCT | CT | DICOM | DICOM Data | Deep learning | Dice Similarity Coefficient | Domain Adaptation | Dose Volume Histogram | Feature extraction | Heatmap Regression | Image Segmentation | Image preprocessing | Landmark detection | MONAI | MedSAM | Medical Image Preprocessing | Medical Imaging | Medical image segmentation | Medical imaging libraries | Multi Vendor CT | Multi-Vendor | Pseudo-labeling | PyTorch | Python | Radiotherapy | Response prediction | SimpleITK | Synthetic data | TotalSegmentator | Transfer Learning | Treatment Response Prediction
Education
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