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streams, contributing to elevated environmental mercury levels and increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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carbon stock estimates within the project “A better check on soil carbon - a novel sampling and measurement approach for improved precision in soil carbon monitoring”. Important parts of the work
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increased human exposure. It is estimated that around 300 tonnes of mercury are released annually through these processes, making them one of the top three sources of anthropogenic mercury emissions worldwide
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employed in the Cybersecurity division with close to 50 members at the department of Computer and Information Science (IDA) . You will work together with Simin Nadjm-Tehrani, professor in dependable systems
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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-form empirical research with large datasets. This includes strong programming skills in Python and experience with at least one additional language used for structural estimation or numerical computing
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sensing data which estimate forest biomass, e.g. from airborne and spaceborne laser scanners, since no in-situ based biomass data with global coverage exist. Who we are looking for The following
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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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, or robot perception. Strong programming skills in Python and/or C++, with experience in robotic software frameworks (e.g., ROS/ROS2). Experience with machine learning or computer vision methods, preferably