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of machine learning to accelerate the design and understanding of 3D-printed multifunctional metamaterials, which exhibit tailored combinations of mechanical, thermal, and functional properties. Highly complex
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in the design of methods and instrumentation for medical diagnostics and therapy based on the interaction of electromagnetic fields with biological tissues, with a strong emphasis on the use of non
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on tungsten samples and candidate tungsten alloys will validate the simulations and guide the design of more dust-resistant materials. Finally, we will use machine learning to integrate simulation and
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the design of efficient material sorting technologies, including optimized illumination, mechanical handling, and integration into existing demolition and recycling workflows, ultimately delivering a
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on the design, optimization, analytical and numerical evaluation of the performance and practical implementation of novel linear and non-linear backscatter-based identification and sensing transponders and