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a B.Sc. in Science, Engineering, Physics, Computer Science or a related field with a focus on medical imaging. It is essential that applicants have excellent interpersonal skills, oral and written
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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transportable. A graduate student with a physics background is needed to perform software-assisted design of system components, write and apply computer programs capable of simulating MRI pulse sequences, and to
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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translational science within a research environment. Outstanding U of A benefits include health, dental, and vision insurance plans; life insurance and disability programs; paid vacation, sick leave, and holidays
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(https://udall.umn.edu). The position will involve: (1) the collection, processing and analysis of data acquired from quantitative motor tasks (gait, gait initiation, bradykinesia, rigidity, etc.) using
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences | Poland | 6 days ago
assessment of solid surface wettability). Advanced experience in digital image analysis. Specific Requirements An application. Completed and signed “Consent to the processing of personal data for the needs
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and processing • Data collection, including in vivo behavioral experiments • Data analysis • Data interpretation • Data presentation, including the creation of figures and writing manuscripts. This may
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conjunction with cell culture, molecular biology and imaging modalities to investigate the cells and vessels of this pathway. The McDonnell lab culture is open and emphasizes collaborative science with high