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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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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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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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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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the Centre to develop and implement robust machine learning frameworks and pipelines focused on predictions based on hybrid data (real+synthetic). Interpreting results and communicating findings effectively
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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR
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, biology, computer science or related disciplines Strong computational skills, including machine learning, e.g. demonstrable project in a relevant field A strong first-author publication record in a relevant
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terrestrial networks, non-terrestrial network entanglement distribution. Your profile PhD degree in wireless communications, signal processing, machine/deep learning or a closely related field in Electrical and
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of competitive research proposals. You should have experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction
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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring