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The project: Non-destructive testing (NDT) is vital to ensure structures are safe and to reduce operating costs by facilitating predictive maintenance. The focus of this project is on improving
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computing and decentralized intelligence where a swarm of nodes learns graph dependencies by effectively integrating the structure of distributed systems into neural network architecture. This approach
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edges,...) with high temperature structural applications. Knowledge of thermomechanical properties (coefficient of thermal expansion) and thermophysical properties (diffusivity, heat capacity, thermal
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structures, the development of effective architecture for machine learning classification of the structures, and reinforcement learning methods for optimizing the structures for particular applications
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the support and contributions from EM through discussions, workshops, and project updates. The project is inspired by Einar Mattsson's goal of utilizing AI to optimize maintenance planning in existing buildings
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safe and structured environments. Plans, implements, and evaluates a daily program which includes designing classroom arrangement for optimal learning, and developing planning materials and activities
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beyond. The research aims to investigate all aspects of biogenic dyes, from the identification of dye-producing bacteria and the characterization of their pigments, through the optimization
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to detect structural anomalies, predict maintenance needs, and optimize inspection schedules to ensure the safety and longevity of critical infrastructures. In a generic way, the candidate will address
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for a Postdoctoral Fellow in Biophysics Your tasks In this position, you will work at the interface of experimental biophysics, structural biology, artificial intelligence, and pharmaceutical formulation
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(PICs) increasingly used in various applications. To allow a smooth design flow for these PICs, optimized compact models are needed. This PhD position is enabling compact models for optical amplifiers and