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of data structures, ETL applications and data pipelines. Model information in a way that customers can easily consume it for reporting and complex analyses. Responsible for enterprise data management
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twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research groups led primarily by Professor Tarek Abdoun and Professor Mostafa
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able to draw on existing structures within the Department of Arts and at LMU Munich and will be expected to build bridges between humanities theory and technical approaches to AI, thereby contributing
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data structures, and supports system development and standardization efforts. The work directly supports clinical, financial, and operational needs across UW Medicine. Position Highlights Primary systems
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(Staff Positions Only): Performs semi-skilled tasks in construction, repairs, and maintenance of structures, equipment, and appliances. Installs and or repairs locks, door hardware, and window dressing
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outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning, computer vision, NLP, etc.) Work with structured and
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Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
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implement end-to-end data pipelines, develop AI models to extract structured intelligence from unstructured social media data, build spatial risk models, and translate these outputs into a secure, user-facing
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stabilisation of the Cr4Te5 phase. In addition to phase engineering, the project will examine how stoichiometry, structural quality and defect formation influence the potential ferroelectric functionalities
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network dynamics, including (i) modeling questions in mathematical neuroscience, with the aim of motivating and testing the theoretical framework, and (ii) inference of network structure from time-series