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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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has access to excellent HPC infrastructure. For more information about LTG, please see: http://www.mn.uio.no/ifi/english/research/groups/ltg/ The successful applicant will benefit from close
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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-funded AI research group “Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data (DeSBi)” development of deep neural networks and machine learning algorithms for the analysis
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applications of neural networks to the analysis of multi-omic data, models for predicting phenotypes using genotype data, biological data integration, etc.. Participation in these projects will include
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artificial intelligence—especially deep learning—offers transformative potential for developing next-generation earth system models. Recent breakthroughs, such as neural network-based short-to-medium term
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Faculdade de Ciências Médicas|NOVA Medical School da Universidade NOVA de Lisboa. | Portugal | about 1 month ago
experience, based on the application documents and in accordance with the following criteria: a) Deep neural networks – 60% Experience, technical knowledge and/or relevant training in the development
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: http://www.mn.uio.no/ifi/english/research/groups/ltg/ The successful applicant will benefit from close collaboration across disciplines and access to diverse application areas through the joint
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) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 11 hours ago
training, interdisciplinary collaboration, and methodological breadth. The interdisciplinary Health Psychology Program (http://healthpsych.charlotte.edu ) includes faculty from the departments