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microstructure in materials. A distinctive element of the project is its data-driven approach. Together with colleagues at Saxion University of Applied Sciences, you will contribute to the development of machine
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of the art in optical transceivers Understanding modeling of the optical modulators and simulation platform for simulation optical transceivers Develop high-bandwidth TX driver architectures for advanced
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systems, you will develop new computational frameworks for data-driven game-theoretic control in large-scale multi-agent systems. These methods will contribute to applications such as multi-actor power
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characterize their mechanical performance and damage evolution. The experimental results will be used to develop constitutive models for the welded interface. In particular, these models should describe
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LiDAR based foundation models to radar, as well as the development of multimodal foundation models incorporating radar. A further challenge is how different radar representations and sensor configurations
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than relying on purely data-driven approaches. The ultimate goal is to develop more accurate and reliable models and methodologies for the design, validation, and deployment of advanced wind farm control
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software developers to integrate data‑driven models with mechanistic or physics‑based models and domain expertise; showing thought leadership on applying data & AI-solutions in the food & biobased domain
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direction is the transfer of representations from vision and LiDAR based foundation models to radar, as well as the development of multimodal foundation models incorporating radar. A further challenge is how
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-driven optical spikes, enabling sensing systems with unprecedented speed and energy efficiency. A central objective of the project is to develop highly sensitive photodetector neurons by combining resonant
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at Saxion University of Applied Sciences, you will contribute to the development of machine-learning models that connect powder characteristics and process parameters with the properties of the final