100 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Aarhus University in Denmark
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible for implementation of machine-learning algorithms for unmanned aerial vehicles; validation
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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in one or more of the languages taught at the department (French, German or Spanish). The successful applicant will strengthen the department’s focus on foreign-language teaching and learning at upper
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be expected to contribute to the departments’ teaching and supervision activities and to teach and supervise on the department’s bachelor’s and master’s degree programmes, particularly the degree
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expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The Department
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are expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The
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computer graphics, or human vision and attention. The posts require research skills in the design of studies, use of methods, research prototyping and data analysis, and you should have documented experience
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education within one or more of the following areas: education and democracy, education for sustainable development, school exclusion and vulnerability, special education and learning, professional formation