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Job: Scientist in Physical Sciences and Canada Research Chair – Tier 2 in Contrast Agents and Biosensors for Medical Imaging and Image-Guided Therapy (one position) - Sunnybrook Research Institute Toronto, Canada ....
NSF s mission is to advance the progress of science, a mission accomplished by funding proposals for research and education made by scientists, engineers, and educators from across the country. ....
Computational Modeling in Medicine In the past few decades, information technology has revolutionized the medical industry. The accuracy with which biological systems and interactions can be simulated and data can be gathered have improved exponentially. Computational modeling has provided ever-more sophisticated data for the field of medical research. This article will discuss this subject. Image Credit: ShustrikS/Shutterstock.com What is computational modeling? Computational modeling is used to simulate and study complex systems using computer science, physics, and mathematics. Numerous variables are programmed into the computational model to characterize the system which is being studied. By adjusting these variables alone or in various combinations, the outcome can be observed, providing valuable data for researchers. ....
Professor/Assistant Professor/Associate Professor in Biomedical Engineering job with KHALIFA UNIVERSITY timeshighereducation.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from timeshighereducation.com Daily Mail and Mail on Sunday newspapers.
The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label- ....