273 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs 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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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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(e.g. using COBRApy or related toolboxes), or a strong motivation to develop this expertise. Data science, AI/ML, and digital surrogate models Experience with data science and machine learning, including
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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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an opportunity to actively engage as a collaborative partner in different projects depending on their interests and expertise. Learn more about the Center and our research, vision, and values here . About the
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required. The position is funded for two years, with the possibility of extension for a third year. Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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research profile within organisational studies, Computer-Supported Cooperative Work, Human-Computer Interaction or related research areas as documented by a PhD dissertation and/or research publications
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systems. Further information on the Department is linked at https://www.science.ku.dk/english/about-the-faculty/organisation/ . Inquiries about the position can be made to Assoc. Prof. Leonardo Midolo
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. We expect applicants to hold a PhD in a relevant field such as techno-anthropology, science and technology studies, human-computer interaction, human-robot interaction, digital health, anthropology