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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position In this role your responsibility is
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application! The Department of Biomedical Engineering (IMT) conducts advanced research at the intersection of engineering and neuroscience, with a focus on the development and clinical application of minimally
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position Do you want to work at the
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to the development of several innovative doping methods. More broadly, the research aims to understand and control how molecular interactions, ions, and charge transfer processes determine the electronic properties
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mission is to encourage innovation and development through which we develop dedicated, innovative student who can improve the world. We have strong links within the corporate and other sectors, which
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processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life
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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position As AI Training Program Officer, you
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, vulnerabilities in isolated systems are analyzed to pioneer new research methods and train both students and professionals. More about cybersecurity research at LiU: https://liu.se/en/research/cybersecurity
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning