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computer vision. Our marine environments are under pressure from rising temperatures, eutrophication, fishing activities, and more. This leads to a decline in biodiversity, destruction of habitats, and
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of novel computer vision methods capable of robustly detecting AUs. Next, the focus will be on novel AI methods for classifying normal vs. pain vs. stress faces. Lastly, head pose detection of horses in
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. The Postdoc project is headed by Professor Kim Lambertsen Larsen and is co-supervised by Associate Professor Thorbjørn Terndrup Nielsen. The project is funded by the Villum Experiment Programme that supports
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program Offshore Drones and Robotics(https://www.energy.aau.dk/research/research-groups/robotics) and the Postdoc position(s) will be positioned in the Esbjerg Energy Section. Research areas will be within
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leading workshop and laboratory facilities, where research and innovation are carried out in direct collaboration with industry to a great extent. The position is offered in relation to the research program
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an advantage, as the candidate is expected to contribute to the Construction Management and Building Informatics education at Aalborg University. For further professional information, please contact Søren Munch
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Postdoc in statistics to develop Bayesian privacy metrics for synthetic health data (2024-224-05725)
scientist, students, and a student helper. The project is a part of the ambitious Novo Nordisk Foundation Data Science Collaborative Programme,“Synthetic health data: ethical development and deployment via
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contain the following: A motivated text wherein the reasons for applying, qualifications in relation to the position, and intentions and visions for the position are stated. A current curriculum vitae Copy
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. The successful candidate must be an outstanding scientist who has already had significant international impact on research and who has demonstrated the vision and creativity to define an independent research
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following discipline areas: creation of multi-scale computational models and fatigue damage models accounting for complex operational loads, experimental methods and studies allowing to observe fundamental