19 data-analitic Postdoctoral positions at Chalmers University of Technology in Sweden
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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Are you passionate about using data and AI to improve human health? Join us in tackling one of the biggest global health challenges of our time – antibiotic resistance. We are offering a three-year
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universities around the world. The department provides a friendly, creative, and supportive atmosphere with a steady flow of international guests. More information about us can be found on our website: http
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. Strategic management of platforms and innovation ecosystems – open innovation, platform orchestration, and multi-stakeholder partnerships in dynamic environments Strategic use of intellectual assets and data
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digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge
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within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences
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computer. The WACQT team at Chalmers currently has about 100 members (faculty, permanent research staff, postdoctoral researchers, PhD students, and undergraduate students). WACQT is committed to promoting
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collaborative manner. Conducting interviews, workshops, and collecting case study data, translating the case observations into analytical representations, writing and presenting high quality papers, and being
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), recovery of critical raw materials, and the synthesis of new materials from secondary sources. More information about the research can be found here: Industrial Materials Recycling – Chalmers Main
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reliable and sustainable components more affordably and more efficiently. Due to the complexity of the research, an integrated framework combining experiments, simulations, and data-driven methods is