26 bayesian-object-detection positions at King Abdullah University of Science and Technology
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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○ Object Detection: YOLO family (v5, v7, v8, v11), Faster R-CNN, RetinaNet ○ Image Processing: Classical and deep learning-based approaches ○ Version Control: Git ○ ML Operations
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successfully delivering critical projects, ensuring that key objectives and planned outcomes are met. This individual should possess strong commercial acumen and a comprehensive understanding of all stages
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the world’s pressing scientific and technological challenges, broadly aligned with the objectives of Saudi Arabia’s VISION 2030. All relevant research areas of Computer Science will be considered, with a
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to addressing the world’s pressing scientific and technological challenges, broadly aligned with the objectives of Saudi Arabia’s VISION 2030. All relevant research areas of Computer Science will be considered
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tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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or a related field with demonstrated relevant expertise or Master of Science/Engineering (preferred) at least 2-3 years of experience in the lab. Apply now » Find similar jobs: KAUST Jobs, Academic and
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contribute significantly to the objectives outlined in Saudi Vision 2030. This alignment should be explicitly demonstrated by emphasizing one or more of KAUST's Research, Development, and Innovation (RDI
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Development Goals and to contribute significantly to the objectives outlined in Saudi Vision 2030. This alignment should be explicitly demonstrated by emphasizing one or more of KAUST's Research, Development