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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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that determine these properties and use this knowledge to develop a robust anodisation process. This process will be tailored to accommodate the wide compositional diversity of recycled aluminium, ensuring
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statistical models (for example principal component analysis) to obtain insights into relationships between physical properties of polysaccharides (composition, molecular weight charge, chain length etcetera
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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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main research topic for this position is to investigate the thermal break-up of pure polymers and composites using a combination of experimental techniques and chemical modelling. For a position as a
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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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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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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