19 phd-mathematical-modelling-population-modelling Postdoctoral positions at Empa in Switzerland
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platform chemicals. Your tasks Integrating carbon cycles and CCUS technologies into a fully sector-coupled Swiss energy system model. Identifying robust transition paths for Switzerland’s energy system
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, to allow us to optimize materials properties. Your tasks Develop multi-material models at the atomic and molecular level to enable us to optimize materials processes and designs Using experimentally obtained
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research at the interface of modeling and simulation of laser manufacturing processes (e.g., laser cladding, additive manufacturing) and experimental validation using advanced facilities (high-speed optical
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ETH Domain. In our Laboratory for Advanced Materials Processing in Thun, near Bern, we are currently looking for an outstanding candidate to conduct cutting-edge research at the interface of modeling
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vivo relevant models for antimicrobial assessment to demonstrate the efficacy of the developed coatings under conditions closely simulating in vivo environment The work aims to advance the fundamental
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of environmentally relevant UFP and MNPs and their comprehensive characterization Assessment of UFP/MNPs impact at the maternal-fetal interface using state-of-the art human models (e.g. placental insert co-cultures
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laboratory in Switzerland. With expertise in reactor design, plasma-based gas conversion for energy and environmental applications and plasma reaction modelling, our interdisciplinary team is poised to drive
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and MNPs and their comprehensive characterization Assessment of UFP/MNPs impact at the maternal-fetal interface using state-of-the art human models (e.g. placental insert co-cultures and explant
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and international, providing an inspiring environment for high impact research. We have internal expertise in (nano)materials engineering, biol-ogy, ex vivo models, biomedical engineering, computational
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single oligomers, protofibrils to mature fibrils in blood require to be correlated in a patient-specific manner with clinical results to build a predictive model for identifying people at risk of