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the following responsibilities: Designing and performing experiments to study skin regeneration, inflammation, and fibrosis using human tissue samples and preclinical models. Applying advanced molecular and
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research, addressing the big data and artificial intelligence challenges of our industry partners. Field of subject for the position: Computer Science Placement: Department of Computer Science and Media
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to develop solutions with real world relevance and impact. This project will be carried out in close collaboration with researchers from the Division of Material and Computational Mechanics at IMS and the
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. Demonstrated skills in Python programming, or other computer programming. Strong interest in data science, such as data collection and curation, modelling. Excellent written and oral English communication skills
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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photoreceptive pathways using mouse as a model system. Methods used in the lab include various in vitro, ex vivo and in vivo approaches, as well as functional and behavioral studies of relevant opsin knockout mice
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thermal conductivity b) Development of hBN thermal interface material that combines a high degree of compressibility and recovery c) Modeling, simulation and characterization of phonon transfer across
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evidenced by recent publications in e.g. Nature Biotechnology – and also provides a stimulating environment for learning computational biology. The successful applicant will in furthermore receive training
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coast and in offshore areas. The position will also include further development of ongoing data analyses within the national data collection program (DCF), which plays a key role in improving our
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in mammals. The Simon lab's primary salamander model is the Iberian newt which has the widest spectrum of regeneration capacity among vertebrates. The lab has established several experimental paradigms