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product quality attributes, such as remaining shelf life. Process measured sensor data of commercial cold chains, analyze data for variability, and reformat data in databases. Use the simulation-based
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experience with X-ray methods and imaging Have preferably some additional experience in the biomedical domain and/or in image processing Have preferably some experience using ML models and tools (e.g
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willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest in hands
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on different topics related to wound healing. Project background Wound healing is a complex process in which a cascade of physiological events takes place to restore injured skin to its full functionality
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. The role of the PhD student at Empa will be the development of synthesis processes using light-based 3D printing of hydrogel-ceramic composites and the identification of process-structure-property
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and inorganic powder-based materials for structural and functional applications. You will study and characterize the powder and formulation dependent granulation process to develop advanced ceramic
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is a key-enabling technology for advanced manufacturing. In order to address increasingly complex demands on joining (dissimilar materials combinations, miniaturisation, extreme operation conditions
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of this project, you will work with a team of scientists with backgrounds in chemistry, cell biology and microbiology as well as with clinical partners on the development of engineered corneal transplant to treat
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. Empa is a research institution of the ETH Domain. Your tasks Your tasks will focus on the design and operation of experiments to investigate the failure mechanisms in wooden structures. This will require