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development in repair and remanufacturing, allowing for longer product lifetime and promoting a more sustainable and circular economy. This project investigates and integrates two crucial elements
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machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be
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understanding of lithium mineral systems is clearly needed to support exploration efforts for this energy critical metal. The PhD student will explore the geochemical behaviour of lithium in the Earth's crust as
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Encryption (FPE), Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Zero-Knowledge (ZK). These applications are usually defined over prime finite fields, and more recently, even over integer
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antennas, where wide-angle beam steering of a narrow high-gain beam is realized by using a large number of active elements, (e.g. 1000-5000 active channels). This provides a very good performance but is on
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the Experimental Zoology Group (EZO) at Wageningen University, which has strong expertise in insect ethology, biomechanics, and sensory ecology. Using 3D videography, EZO has previously unraveled and modelled
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critical metal. The PhD student will explore the geochemical behaviour of lithium in the Earth's crust as a function of variables such as composition, temperature, pressure and oxygen fugacity. The candidate
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. An integrated understanding of lithium mineral systems is clearly needed to support exploration efforts for this energy critical metal. The PhD student will explore the geochemical behaviour of lithium in
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and element geometries that preserve polarization separation and suppress cross-polarization components. A high-level planning of the project includes the following phases divided over a period of 48
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the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be repaired, reused, or discarded requires