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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
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of the mineralogical model of the Earth's whole mantle (upper mantle, transition zone, and lower mantle) clarification of the differentiation process of the early Earth from fully molten state exploring the internal
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. The department hosts more than 150 scientists who use a variety of research models to study the biological mechanisms governing human physiology and disease. In addition to its research mission, the DBS plays a
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the cryosphere domain. The first topic involves analyzing SAR interferometry to generate time series of digital elevation models, which will facilitate the quantification of changes in glaciers and ice sheets on a
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modeling and experimental testing of composite wall panels under axial compression. The planned tasks for PhD are as follows: Formulation of a numerical modeling framework to model interlayer and interfacial
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communication skills, both written and verbal. Business-oriented mindset with an interest in commercializing software innovations. Desired Skills Experience in numerical modeling of geologic reservoirs and
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learning-based generative models and physics simulation) and data inference (including segmentation, classification, parameter inference and mesh fitting) based on data-driven and (bio)physics-informed