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). This includes studies conducted within the Division as well as national and international collaborative network studies in which Division members are involved. The incumbent will also be involved with supporting
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learning, Uncertainty-aware and probabilistic methods Researching efficient inference algorithms and neural network optimization methods for resource-constrained embedded platforms (edge AI); Creating core
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rewarding career. Job Summary The Canadian Pharmacogenomics Network for Drug Safety (CPNDS) is a Pan-Canadian network for research with a specific aim to prevent serious adverse drug reactions (ADRs) by
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in Wales. These are Tackling Inequalities, Community Wellbeing and Environment and Net Zero. Alongside these, we lead a Health Determinants Research Collaboration with Rhondda Cynon Taf Council focused
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systems engineering, electrical engineering, or other relevant areas Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systems
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understanding of data evaluation, modeling, and interpretation of complex datasets Ability to work independently as well as collaboratively in an interdisciplinary and international research environment Very good
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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strive for the highest level of complex care for children from diagnosis and treatment; provide outstanding education and training for students, postgraduate scholars, and physicians; and nurture the
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in complex digital projects - Conocimientos en testing de usabilidad y análisis de métricas UX mediante herramientas especializadas (Maze, Useberry) // Knowledge of usability testing and UX metrics