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, compression, learning, and inference for classical and quantum data exchanged through classical and quantum networks. The objective of the PhD study is to explore and address research and design challenges
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head-on. We will reinvent generative cooperative vision and semantic compression methods so fleets of intelligent machines can perceive the world robustly, efficiently, and in a trustworthy manner—even
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on purified CO₂ gas as feedstock, necessitating costly and energy-intensive capture, purification, and compression processes. Furthermore, high-efficiency alcohol production has primarily been demonstrated
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. This will involve investigating techniques for model compression and efficient inference to enable on-board condition monitoring directly at the wind turbine, reducing data transmission requirements, central
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or 3-dimensional spaces, enabling insights about the underlying structure and distribution of the data. However, due to the heavy data compression into a space with only 2 or 3 degrees of freedom
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pressure shock waves which induce compressive residual stresses in the structure, thereby improving the surface hardness and the resistance to fatigue cracking and to corrosion. LSP is more effective than
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(EBSD, EDX, SAXS, TEM) and ex situ/in situ nanoindentation-based techniques (micro-tensile/compression). The insights gained from this investigation will be crucial for optimizing existing Mg-RE alloys
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is, however, the transportation and storage. Current methods rely on liquid compressed hydrogen, which requires high pressures or low temperatures. This project will computationally explore catalyst
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documentation are not in a Scandinavian language or English, the applicant must upload certified translations. If attachments exceed 25 MB, they must be compressed or provided as links. Applicants with foreign
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flow regime ranging from steady laminar to unsteady turbulent configurations, there is also potential to extend the analysis to compressible flows and structural analysis. This research is highly