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for in vitro disease modeling. Our group integrates mechanical design, materials science, and bioengineering to build functional 3D organoid systems and reduce reliance on the use of animal models. We
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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. Dagmar Iber, which uses advanced imaging and computational tools to develop data-driven, mechanistic models of biological systems. Located in Basel, the Department of Biosystems Science and Engineering (D
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100%, Zurich, fixed-term The Chair in Nonlinear Dynamics at ETH Zürich is seeking a highly motivated PhD student in the area of data-driven model-reduction for high-dimensional nonlinear physical
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of the Institute of Neuroinformatics (INI), ETH Zurich. Our lab's research focuses on the dynamical organization of the human brain activity in neurological disorders and behavior. We aim at identifying and modeling
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Dynamics , which pursues a broad range of experimental, numerical and theoretical research efforts in a friendly and inclusive environment with state-of-the-art infrastructure. The blood-brain barrier (BBB
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. Dagmar Iber, which uses advanced imaging and computational tools to develop data-driven, mechanistic models of biological systems. Located in Basel, the Department of Biosystems Science and Engineering (D
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for retroviral or lentiviral transduction, cell culture, and immune functional assays; supporting the development of in vivo models, such as adoptive T-cell transfer in mice, for preclinical testing of engineered
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and running field transplant experiments, and ecological niche modelling of focal species. The PhD candidate will also have the opportunity to develop their own analyses within this broad theme. Finally
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transduction, cell culture, and immune functional assays; supporting the development of in vivo models, such as adoptive T-cell transfer in mice, for preclinical testing of engineered circuits where relevant