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The postdoc fellow will conduct research in the intersection of AI/Machine Learning and Software Technology. The advertised position will be placed in the DISTA research group (https://lnu.se/en/dista
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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need you to have a high aptitude and passion for learning how to use new technology, and working productively with others who may have different skillsets and knowledge. You will need to be comfortable
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the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site nature-based solutions that enhance local
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in: Udder health and animal welfare Digital learning and employee education Big data and tech in agriculture Bilingual communication (English & Spanish a plus) This position is available now. If you're
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on different platforms, including forest machines, robots, UAVs, and hand-held devices. via Unsplash Professional qualifications (required) PhD in Computer Science, Machine Learning, or a related field
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on methods such as functional connectivity analysis, brain network analysis, or machine learning; Excellent scientific writing and communication skills in English; Ability to work independently while
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 19 hours ago
of executive control, and contextual modulators of executive control like emotion and motivation. We use and develop advanced computational methods, including “big data” statistical methods, machine learning
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning