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Field
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the transformation of cities over time? Do you want to find out which kind of governance arrangements drive or hinder such transformations? Then this PhD position ‘Governance of Complex Urban
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-beam SEM that combines high-throughput imaging with integrated cathodoluminescence (CL) detection enabling large-area imaging of biological samples. This four-year research programme will proceed in
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learning techniques, developing attention-based transformer models that will directly classify events from the low-level data, rather than high-level objects, and which may ultimately include higher-level
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accessibility of imaging services. The RHYTHM project addresses key challenges in justification, optimisation, availability, and access to imaging services. Key objectives include the development of evidence
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systems has been an object of intensive research. While verification support for these languages does exist, it is limited in either the guarantees it can provide or in its level of automation. Lifting
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the low-level data, rather than high-level objects, and which may ultimately include higher-level tasks such as Higgs boson mass regression for the analyzed data. The boosted object analysis will have a
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core objective of this research is to improve polarization purity by addressing both the generation of orthogonal polarizations in transmit mode and the minimization of inter-polarization coupling
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aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object. Unlike traditional robots
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high-throughput imaging with integrated cathodoluminescence (CL) detection enabling large-area imaging of biological samples. This four-year research programme will proceed in three main phases. In
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trunk around an object. Unlike traditional robots, which rely on rigid links and localized interaction points, soft robots are composed of deformable materials that can conform to complex geometries and