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focuses on developing advanced optimization and control strategies (e.g., deep reinforcement learning) for large-scale sustainable grids, to enhance overall system stability, flexibility, and resilience
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properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
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apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
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research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows, innovative technologies for biomass conversion, neural network systems, and artificial
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the Department of Energy Sciences. At the division, we conduct research in various fields, including research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows
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graphene-based field effect transistor sensors with biological receptors for infection biomarkers, and optimize this technology for diagnosing infections in the wound settings. As a postdoctoral researcher
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include: Benchmark study: Compare and evaluate methods and models for digital twin simulation in autonomous shipping, and integrate them into a cohesive model. Energy optimization: Develop a dynamic energy
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methods and models for digital twin simulation in autonomous shipping, and integrate them into a cohesive model. Energy optimization: Develop a dynamic energy optimization model for hybrid and electrified
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XLZD. XENONnT is a dual-phase liquid xenon TPC at LNGS with a 5.9-tonne active mass, optimized for WIMP detection with sensitivity to spin-independent cross sections down to ~10⁻⁴⁸ cm². Its low
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by, computational chemistry to optimize and predict process parameters. The Department of Engineering and Chemical Sciences, with approximately 60 employees, is a part of the Faculty of Health, Science