56 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Helmholtz-Zentrum-Geesthacht" positions at Aarhus University in Denmark
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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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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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The Department of Biomedicine at Faculty of Health at Aarhus University invites applications for a position as an academic employee within image analysis and IT infrastructure for imaging data as
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Mechanics and Turbulence” group and conduct research on data-driven techniques for turbulence modeling in LES and RANS. The initial contract will be for one year, with the possibility of an additional one
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various data analysis tasks. Your work will focus on carrying out data preprocessing, data wrangling, and carrying out analyses using R (and sometimes Python). Example projects you will be working
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unified data framework for microbial carbon dioxide conversion, integrating data from methanogens, acetogens, and hybrid projects for standardization, kinetic/thermodynamic measurements, and predictive
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the referee’s contact information when you submit your application. We strongly recommend that you make an agreement with the person in question before you enter the referee’s contact information, and that you
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in the fields of: Construction Management and Lean Construction. Production Planning and Control. Data-driven analytics and management of construction operations. Construction Informatics, including
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) outlining a research project addressing the history of Danish botany and the Flora Danica volumes in the period 1840–1900 within the statement of future research plans and information about research
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental