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levels. Apply a strong understanding of enterprise cybersecurity and technology security challenges. Utilize knowledge of computer network defense tools and processes, including leading commercial vendor
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transition to a net-zero economy, electrical power systems are rapidly decarbonising supply whilst electrifying carbon-intensive demand. This is accelerating the integration of low-carbon technologies (LCTs
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levels. Apply a strong understanding of enterprise cybersecurity and technology security challenges. Utilize knowledge of computer network defense tools and processes, including leading commercial vendor
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
-prüfung (BAM) is a materials research organization in Germany. Our mission is to ensure safety in technology and chemistry. We perform research and testing in materials science, materials engineering and
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to up to 30 kHz leads to the massive production of data, for which fast (even quasi-instantaneous) processing via neural networks is crucial. Objectives and expected results: The PRICELESS PhD project
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Description We are seeking an outstanding Doctoral Candidate (DC) for a fully funded studentship as part of the prestigious Horizon Europe MSCA Doctoral Network “Advanced Network Connectivity using Harmonious
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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 ICT Services
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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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-to-day operations of the project, the Research Assistant will help build strong research networks, contribute to securing research funding, and produce high-quality outputs including publications and
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software