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on developing an advanced, AI-integrated methodology for the design of Large Underground Structures (LUS), addressing the limitations of traditional empirical approaches. These structures are critical to sectors
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for stormwater management that leverage modern technologies and sustainable practices. This PhD position offers a unique opportunity to advance urban stormwater engineering through data-driven and nature-based
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tectonic and metamorphic history. Although it has been documented that graphite can be mobilized and accumulated along structural weaknesses such as ductile shear zones and fold hinges under high-grade
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outside academia. This PhD position focuses on the intersection of structural engineering, artificial intelligence, and computational design. The project aims to advance the autonomous generation
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they release their toxic cargo to kill the cancer cells. Methods include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse
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systems such as fuel cells and batteries. Intelligent control and management systems will be designed to enhance efficiency, sustainability, and seamless integration. Developed models and strategies will be
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Additional preferred expertise and experience Experience with longitudinal data analysis and advanced statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural
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statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural equation modeling). Familiarity with the opioid and endocannabinoid system and topics related
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include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse immunological assays, advanced flow cytometry, fluorescence
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advances, challenges like time-consuming segmentation and model complexity limit clinical adoption. This project aims to streamline segmentation and develop real-time biomechanical assessment tools, bridging