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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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engineering, geoinformatics, computer science, or a related field with a focus on deep learning applied to remote sensing or geospatial data Documented experience with deep learning model development (e.g
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hold a PhD in oceanography, marine ecology, computer sciences, data sciences or similar. We expect that you have: Expert knowledge on network modelling, especially aimed at ecological applications Strong
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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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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has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct
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. Software or code development, incl. artificial intelligence and machine learning. Automation and robotics, incl. safe human-machine interaction. Serious gaming, incl. AR/VR. Life cycle analysis. You are
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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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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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