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                -to-end virtual supply chains. This project also aims to optimize other thermal processes using digital twins, such as thermally-steered drug delivery processes for applications in oncology. PhD position 
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                using digital twins, such as thermally-steered drug delivery processes for applications in oncology. Your tasks Your tasks (the priorities will be flexibly decided depending on the concrete opportunities 
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                approach to (1) better understand and steer refrigeration processes or packaging design by optimizing for product quality loss and waste, and (2) connect the improved quality preservation from digital twins 
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                engineering applications. Empa's Centre for X-ray Analytics develops X-ray analytical and imaging methods for understanding materials structure in material, life- and medical sciences. This is a joint PhD 
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                , interact with the other 4 PhD students of the project at ETHZ and TU Delft, and use state of the art methods such as Nanoindentation, Raman Spectroscopy, Digital Light Processing, Two-Photon Lithography 
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                in cold sintering of ferroelectric ceramic materials. This project focuses on the synthesis of ceramic powder and the densification using the cold sintering processing method. The focus of the project 
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                of ceramic powder and the densification using the cold sintering processing method. The focus of the project is the development ferroelectric lead free ceramics sintered below 500°C and the analysis 
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                the supervision of Dr. Paula Navascués. The PhD Student will be enrolled at ETH Zürich, with Prof. Salvador Pané i Vidal as academic supervisor (Department of Mechanical and Process Engineering, D-MAVT). You will 
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                enrolled at ETH Zürich, with Prof. Salvador Pané i Vidal as academic supervisor (Department of Mechanical and Process Engineering, D-MAVT). You will be employed at Empa in St. Gallen full-time for three 
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                related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy