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At the Technical Faculty of IT and Design, Department of Electronic Systems, one or more PhD stipends/Integrated PhD stipends in representation, compression, learning, and inference for classical
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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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developing and training large scale models (cluster-based or distributed training). Your primary focus will be on object sensitive neural compression techniques for low-bandwidth streaming and robust video
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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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starlight. In ICE-EEVOLVE , we seek to unravel how chiral organic molecules, trapped in amorphous ice matrices, evolve from molecular clouds through star-forming regions to planetary systems. Laboratory
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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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advanced microstructural characterization (EBSD, EDX, SAXS, TEM) and ex situ/in situ nanoindentation-based techniques (micro-tensile/compression). The insights gained from this investigation will be crucial
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resistance mechanisms, while also not affecting surrounding healthy tissues. Implantable or injectable hydrogel matrices are able to transport therapeutic vectors to the tumor site and release them upon
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group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods, vectorized query execution, indexes for interactive data analysis
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the broad area of analytical database management systems. Our research group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods