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Field
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of the PhD project is to investigate how biopolymers and biocolloids can be tuned into stimuli-responsive reactive and non-reactive inks for advanced assembly (3D printing) of bio-composites. We envision
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well as the disassembly or reassembly of the materials into new composite structures. We envision that the generated knowledge will advance the field of bio-based and sustainable material technologies and contribute to new
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of practical solutions, economics and legislative constraints, the simplistic approach to reducing enteric CH4 production is to modify dietary composition (Hart et al., 2015). This can be accomplished by
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analysis is complemented and correlated with circulating tumor DNA (ctDNA), as well as immune cell composition in serial blood samples collected during therapy. The work is carried out in close collaboration
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: We're excited to announce an open PhD student position in Robert Vacha's Research Group! Eukaryotic and prokaryotic plasma membranes exhibit inherent asymmetry, with distinct lipid compositions between
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complementary methodologies (corpus data and offline experimental measures). On the theoretical side, the project will develop a formal compositional model that generates the observed parameters of variation and
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-ceramic composites in close collaboration with a PhD student at the Biomaterials Engineering Group at ETHZ and the identification of process-structure-property relationships enabling the efficient design
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. You already have good background knowledge on atmospheric composition and greenhouse gas cycles and experience with isotope measurements. Next to this you find yourself in one or multiple
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these composite structures. End-of-life industrial filtration felts are heavily contaminated with hazardous by-products, including heavy metals, biofilms, fats, solvents, dyes, and reactive particulates
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models considering networks of patches and their species and interactions composition to predict spatial and temporal community structure across restoration gradients, aimed at developing a predictive