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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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tools for the prediction of composite manufacturing processes. You will work on development of algorithms, custom written codes, application of commercial finite element software and development of user
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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Computer Vision algorithms. Experience using urban building stock modelling and urban digital twins What you will do: Design & Develop: Create data structures for detailed, spatialised construction component
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studies and development of prototypes for confidentiality with algorithms and models for attach detection. Application in secure communications and 5g and 6g networks. Where to apply E-mail andres.mlopez
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supported by COMMLab, 6GSPACE Lab, HybridNetLab, QCILab, TelecomAI Lab, CSAT Lab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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courses with minor algorithmic components and primarily programming courses with a focus on bioinformatics methods. Such graduate courses seek experienced bioinformatics, biotech, and data science
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), and the European Innovation Council (EIC). Project description Superconducting quantum circuits is a pioneering field of research to develop cutting-edge quantum technology, especially quantum
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation