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screening approaches. The PhD student will develop and apply analytical workflows to characterize complex food matrices. The project includes i) developing and optimizing screening workflows; ii) improving
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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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strategic research areas: cell and molecular biology; evolution and biodiversity; precision medicine and diagnostics; and epidemiology and biology of infection. For more information, please visit: https
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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to investigate all aspects of biogenic dyes, from the identification of dye-producing bacteria and the characterization of their pigments, through the optimization of their production and application
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to investigate all aspects of biogenic dyes, from the identification of dye-producing bacteria and the characterization of their pigments, through the optimization of their production and application
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, spectroscopic signatures, microstructural images, processing conditions, and macroscale performance will be used for the optimization of materials. The candidate will collaborate extensively with in
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on developing biochar as a sustainable feedstock for hard carbon anodes in sodium-ion batteries. In collaboration with Besca AB (https://www.bescacarbon.com/ ), this project explores microwave plasma technology