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walls reinforced with iron-based shape memory alloys (Fe-SMAs). The PhD project aims to develop a nonlinear finite element modeling framework to simulate the mechanical bond behavior between 3D printed
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for complex engineering systems Exploring the use of state-of-the-art surrogate modeling techniques (e.g., stochastic emulators) within optimization frameworks to handle computationally expensive simulations
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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
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100%, Zurich, fixed-term We would like to announce a PhD position in molecular modeling of intrinsically disordered proteins constrained by EPR, cross-linking and NMR. Proteins in disperse and
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aims to systematically address these challenges by building chemistry-informed machine learning models for selectivity and reactivity prediction. These models will also provide valuable mechanistic
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initiation and growth. Through a combination of theory, simulations, and experiments, we aim to build predictive models of fracture and provide a foundation for designing soft materials with enhanced
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learning and a combination with hydrological simulations. From this data, a high-resolution moisture index is generated, serving as an early warning indicator for flood-prone catchments. Finally, this
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related research directions in the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural
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, modeling, and visualization. Strong publication record in relevant scientific journals. Language Requirements: Proficiency in English (spoken and written). Knowledge of a Swiss national language is an asset
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record in computational biology and the modeling of biological systems. Their research focus should be on the development of computational approaches to biology, with an emphasis on AI and image analysis