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the application. Required qualifications: Knowledge of marine biology and marine ecological processes Strong skills in statistical modelling and spatial data analysis Capacity to work with complex datasets Other
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for the sustainable use and conservation of coastal and marine ecosystems. In particular, you will contribute to the development of end-to-end ecosystem models and ecosystem data analysis tools. About the position You
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Computational Arts, Music, and Games within the DSAI division. About the research project This position is related to investigating learned cultural representations in data search spaces of generative AI models
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for advanced courses, international research visits, and networking across Sweden’s top universities. Information about the research group The Computer Vision Group at the division of Signal processing and
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work. A model is to be developed to estimate the material mass breakdown for various cell designs and cell formats. The model will be validated from teardown analysis of commercial lithium-ion battery
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) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations
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challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations, and molecular design
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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Master’s degree in Applied Mechanics, Mechanical Engineering, or a closely related field. Strong knowledge of fluid mechanics, CFD, turbulence modelling, and structural mechanics. Understanding
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to construction processes, policies, or material flows. Familiarity with research or practice at the intersection of building production methods, circular business models, and sustainability transitions. Experience