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metrics (efficiency, bandwidth, angular response, and robustness). In parallel, the candidate will gain in-depth knowledge of analytical modeling techniques and adjoint-based optimization methods for high
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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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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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parameters. In parallel, the candidate will gain in-depth knowledge of time-modulated photonic media, nonlinear optics, adjoint-based optimization strategies for high-dimensional inverse design, and realistic
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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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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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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