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Field
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-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate and open
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contact Holger Voos at: Your profile Solid experience / knowledge in the following areas: Design and modeling of soft materials and soft robotic components Modeling and simulation of drones Mechatronics and
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simulate the impacts of extreme weather using future-looking climate-based projections. It aims to identify cost-effective, climate-resilient investment pathways for the GB’s whole energy system. The project
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chemistry and life cycle thinking in early decision-making. AI-based methods and hybrid models, which integrate data-driven and mechanistic modelling, hold significant potential to enhance prospective
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31st July 2025 Languages English English English The Department of Civil and Environmental Engineering has a vacancy for a PhD Candidate in Hydrological and Water Resource Modelling in the Himalayan
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approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with
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characterization and benchmarking protocols for their suitability to provide efficient cost functions for the optimal control of gate calibration in full-stack quantum computers and simulators. Based on your
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of agricultural innovation, exploring how advanced weather forecasting, crop modelling, and digital decision tools can be harnessed to support smarter, more agile on-farm decisions. It offers a unique opportunity
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Angeles (USA). We seek three brilliant PhD candidates who are passionate about using computer simulations and performing data analysis of various spacecraft observations. Despite their modeling nature
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against