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error-correcting codes, establishing fundamental performance limits, and building practical decoding algorithms and architectures. Your research will sit at the interface between the classical and quantum
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: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural
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sovereignty, and cyber-electromagnetic resilience. The PhD researcher will primarily work within Tilburg University’s AI research infrastructure, focusing on algorithm development, model training, and
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scheduling algorithms for fast control and reconfiguration of the optical AI compute clusters. Realize a small-scale compute cluster lab testbed to demonstrate and evaluate the performance of the innovative
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learning paradigms. The framework will support rapid prototyping, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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of service. The project is conducted in collaboration with two clusters at the Department of Mathematics and Computer Science of TU/e: Data and Artificial intelligence . Novel learning algorithms will
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-static beamforming methods, and semi-tomographic reconstruction algorithms that enable high-quality 3D visualization of the abdominal aorta. In addition, you will develop algorithms for segmentation
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sovereignty, and cyber-electromagnetic resilience. The PhD researcher will primarily work within Tilburg University’s AI research infrastructure, focusing on algorithm development, model training, and
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master's degree in computer science, mathematics, or related subjects. A solid background in cryptography, algorithms, discrete mathematics, or a related area. Strong analytical and problem-solving skills