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and Images (PSI), within the research group EAVISE. The project explores processing audio under motion. For stationary sound source and receiver conditions, audio processing has seen strong advances in
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that enable networks to autonomously mitigate failures and to dynamically adapt to changing conditions, while ensuring reliable service delivery across cloud/edge/terrestrial/non-terrestrial infrastructures
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detectors, can strengthen conventional SLAM-based tracking to improve localization under complex, radiation-rich conditions. Furthermore, the PhD will explore how this enriched representation can be delivered
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the datasets with key conditions that will have been identified as missing in the existing databases. This will help improve the quality of the datasets and therefore of the learning model. [Month 12 – 30]. 5
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heterogeneous and multi-domain environments. The PhD candidate will explore innovative mechanisms that enable networks to autonomously mitigate failures and to dynamically adapt to changing conditions, while
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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, GC), and process modelling. Experience in experimental design, parameter optimisation, and statistical analysis is valued. Further, all MSCA-DN specific eligibility conditions apply: Mobility rule. Not
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pipeline by designing and validating generative models (e.g. diffusion-, autoencoder-, and GAN-based approaches) that can infer missing anatomical information and realistic, biomechanics-aware
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and