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the key electromagnetic coupling effects between layers and enables fast prediction of their tunable response without relying solely on full-wave simulations. To create efficient adjoint-based optimization
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. To create efficient adjoint-based optimization methods for tunable metasurfaces that provide rapid sensitivity/gradient evaluation, support high-dimensional design spaces, and allow systematic optimization
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functionalities while restricting control to a single, or otherwise limited, discrete set of tuning parameters, enabling practical global actuation and robust device operation. To design optical metasurfaces and
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operation. To design optical metasurfaces and material platforms exhibiting time-varying responses. Using adjoint-based optimization and spatial structuring, to realize complex time-modulated medium dynamics
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management This particular call emphasizes a knowledge of AI research, performance optimization of AI training and inference, and hands-on-experience with PyTorch or similar. This should be complemented with
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Finland. Applications are welcome from all areas of operations research, including but not limited to optimization, mathematical programming, analytics, data-driven decision-making, stochastic modelling
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aims to strengthen its leading role in systems and operations research in Finland. Applications are welcome from all areas of operations research, including but not limited to optimization, mathematical
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integration with classical HPC; accelerator platforms and accelerated computing; parallel, distributed, and HPC programming models and languages; software design, verification, and optimization; energy