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Field
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technical 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
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and networking. Opportunities for international mobility may be available. The candidate may be asked to provide limited teaching assistance for the courses GEO4360 Field Methods in Hydrogeology and
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of MishMash. The appointed candidate is expected to collaborate across the MishMash network, and participate in all relevant MishMash activities. The main purpose of a postdoctoral fellowship is to provide
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approaches to fabrication prediction, structural and material optimisation, computer vision for process monitoring and quality assessment, and the integration of machine learning methods within design
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English-speaking program. 2. Demonstrated experience in cybersecurity, computer networks, or artificial intelligence. 3. Demonstrated research experience as demonstrated by publication records. 4
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Department: Electrical, Computer and Biomedical Engineering Position supervisor: Dr. April Khademi Contract length: 1 year (with possibility of extension) Hours of work per week: 36.5 About Toronto
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communication systems. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on