59 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Aarhus University in Denmark
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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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computer graphics, or human vision and attention. The posts require research skills in the design of studies, use of methods, research prototyping and data analysis, and you should have documented experience
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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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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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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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tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise
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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