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Postdoctoral Fellow in Medical Image Segmentation and Multiphysics Modeling for MR-Guided Brain Hyperthermia - Radiation Oncology - ( 260000UP ) Position Summary The Department of Radiation
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. Perform image segmentation, anomaly detection, and extraction of biomarkers for disease classification. Design deep learning models for multimodal image registration and alignment of ultrasound with
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2D/3D/4D image parsing, segmentation, and building robust computer-aided detection systems Proficiency with modern deep learning frameworks (PyTorch, TensorFlow) and related tooling Demonstrated
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writing and in talks. Preferred Qualifications Experience with segmentation of volumetric image data, and with the practical difficulties of annotation, class imbalance, and small training sets. Familiarity
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Your Job Develop machine-learning–based workflows and scientific software for segmentation, species classification, and lineage tracking in multi-species time-lapse microscopy data Optimize models
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such as image quality assessment, view recognition and classification, image segmentation and registration, and automated cardiac condition detection and diagnosis. 4. Work closely with clinical
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image data (e.g., segmentation, object detection, feature extraction). Familiarity with Thermo Fisher Scientific (or FEI) instruments (e.g., Helios platform) or equivalent Tescan, JEOL systems. Prior
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monitoring. Treating ultrasonic RF data from wearable ultrasound devices using advanced beamforming techniques. Develop image reconstruction algorithm and investigate using time series ultrasound image
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compared to identify and evaluate early pathologic processes. Challenging aspects include image segmentation of the lung, lobes, airways, and vascular tree; extending our tissue characterization
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Qualifications PhD in Computer Science or Electrical Engineering received within the last 3 years. Essential experience includes Python, modern techniques in visual recognition and image segmentation