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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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                learning, optimization algorithms, and interoperability frameworks for optimal energy management across Europe. KTH leads technological landscape analysis, multi-energy investment planning tool development 
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                sustainability but also pose serious challenges in ensuring their reliability and fairness. Addressing these societal-scale challenges demands for novel optimization and control methodologies that can meet their 
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                /thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop 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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                system. DBTMI stands for "simultaneous digital breast tomosynthesis (DBT) and "mechanical imaging" (MI). This project aims to support clinical adoption of DBTMI by (1) Optimizing the DBTMI prototype; (2 
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                ). This project aims to support clinical adoption of DBTMI by (1) Optimizing the DBTMI prototype; (2) Performing a clinical study of DBTMI vs DM with 600 women; and (iii) Optimizing DBTMI reading and exploring AI