51 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Aarhus University
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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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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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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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activities of the Department and faculty. Qualifications and Specific Competences The ideal candidate has: A PhD in Computer Science, Informatics, Computer Engineering, or a related discipline Strong
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This is a full-time (37 hours/week) on-site role located at Åbogade 34, 8200 Aarhus N, Denmark for a Postdoctoral Fellow at the Department of Computer Science, Aarhus University. The postdoctoral
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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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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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employees, 500 PhD students and 160 technical/administrative employees who are cooperating across disciplines. As a Postdoctoral researcher, you will be working at Aarhus University Hospital or another
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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