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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data
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large language models (LLMs)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create
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employment benefits at Linköping University is available here. Union representatives Information about union representatives, see Help for applicants . Application procedure Apply for the position by clicking
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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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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
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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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)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create multimodal systems. The emphasis is
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representatives, see Help for applicants . Application procedure Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than 24th of September 2025
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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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Description: Broadleafification, i.e. the process of increasing the share of broadleaf tree species within conifer-dominated forests, is a promising strategy for biodiversity conservation in managed (hemi