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Field
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brain injury are widely used for neurotrauma research and therapeutic development. Today, the mechanistic pathway of experimental neurotrauma is largely unknown and yet little biomechanical reference
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this position, you will develop agentic AI solutions that can autonomously configure and deploy 6G and edge-cloud functions, using digital twins and advanced validation techniques. You will work closely with
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into ongoing efforts at SLU. We are looking for a colleague who is ambitious in technical development and highly motivated to continue an academic career in remote sensing, with strong expertise in tree
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sustainable societal development. With us, research and education are conducted with an eye towards the future. Our proximity to the business world, both locally and globally, gives us a wide reach and the
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computational, theoretical and/or observational projects, to develop and deploy cutting-edge machine-learning and AI methods for astrophysics and cosmology, enabling precision tests of fundamental physics with
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Landscape Architecture Planning and Management (LAPF), along with collaborators in Norway, The Netherlands, Germany and Estonia. You will be responsible for designing, developing, conducting, and analysing
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cytogenetic techniques, and functional models of somatic genetic changes to understand how childhood leukemia arises and develops. Our long-term aims are to use our increased understanding of leukemia biology
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for applications in virtual reality, gaming, digital assistants, and social robotics. We build on recent breakthroughs in spontaneous speech synthesis and gesture generation based on deep generative models to train
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research group has a long standing interest in integration of multi-modal data, in which you will also receive training from a senior colleague. The opportunity to develop Digital Twin projects are also open
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availability of high-throughput genomic data, the lack of advanced analytical frameworks has hindered forensic efforts. This project aims to develop and apply AI-based methods to predict the origin and dispersal