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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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The Department of Ecoscience, Aarhus University, invites applications for a full-time postdoc for a period of 1.5 years. Expected start date and duration of employment This is a full-time position
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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
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The Daasbjerg research group at the Department of Chemistry, Aarhus University, is seeking a candidate for a 31-month postdoctoral position. This position focuses on AI/machine learning to develop a
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? Then the Department of Electrical and Computer Engineering invites you to apply for a 2 year postdoc position bridging research with industrial implementation and innovation. Expected start date and
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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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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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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-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