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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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, Matlab, C++) for developing new simulation frameworks or image processing algorithms Experience in or willingness to learn independently operating additive manufacturing systems (DED and LPBF), including
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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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. You will perform microstructural characterization of dry coated electrodes using physical and machine learning based methods and the electrochemical assessment of the electrodes in battery cells. Your
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger
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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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design Interest in and comprehension of Earth system processes and environmental impacts Excellent communication and collaboration skills and fluency in English. Our offer We offer a dynamic and
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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