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includes the following tasks: Develop computer-aided design software for modular construction of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models
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grasslands and evaluation of land-use intensity, Expertise in classification with machine-learning methods, statistics, spatial analysis and land-use modeling, Experience and interest in conducting fieldwork
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analysis and biomedical data analysis, with demonstrated experience in organ segmentation from medical images, using both traditional and machine learning–based methods, and creation of large segmentation
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inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation and maintenance prediction and integrate analytics
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At the Technical Faculty of IT and Design of the Department of Sustainability and Planning, Copenhagen, a position as Postdoctoral researcher in Geospatial Machine Learning for Predicting Land Use
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the ability to perform complex data analyses. Has experience with implementing computer-based experiments as well as field experiments. Has professional proficiency in English, both written and spoken
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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
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of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication