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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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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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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
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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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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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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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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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-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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/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
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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