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the mobility, ubiquity, security, and interactivity of computers, data, software, and users. The pervasive computing paradigm enables technologies such as sensors, actuators, and computers to take a back seat
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unintentionally bring about macro-level outcomes. The group at IAS focuses on the dynamic processes that lead to a concentration of individuals with certain socio-economic or ethnic characteristics in different
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, biophysics, and computational biology. You will work closely with an experienced team and collaborate with experts in molecular biology, hormonal signaling, microscopy, biomechanics, image processing and
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facilitate data sharing among actors involved in a new circular flow of flat glass. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science
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. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science, or possibly design or cognitive science as main subject) and one at Tema Technology
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. Our group is fascinated by one of biology’s deepest questions: how does development—the process by which genes and environments build organisms—shape the potential for populations to evolve? We study
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to assimilate knowledge at the research level. Understanding and experience in machine learning and computer vision. Knowledge, experience, and strong interest and in AI and XR development. Knowledge and
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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consequences of keel bone deviations: What impact do these have on hen behaviour and wellbeing? high-tech welfare assessment: Help develop a non-invasive computer vision method to track and analyze how hens move
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cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning