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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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, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to optimize other thermal processes
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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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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
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match the chemical, mechanical, and morphological properties of native bone. The role of the PhD student at Empa will be the development of synthesis processes using light-based 3D printing of hydrogel
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deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy infrastructures, or urban energy applications is a strong asset. You are self-driven, creative
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passionate about working in an interdisciplinary field, and fulfil the following criteria: Completed Master`s degree in physics, chemical engineering, biomedical engineering or similar, you have strong hands
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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