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Qualification: The candidate should possess a MSc. Degree or equivalent in Engineering, Computer Science, or related fields. Experience: The ideal candidate should have some knowledge and experience
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Qualification: Master in Computer Science and/or Cybersecurity or equivalent degrees with expertise in at least one of the above areas (dependability, real-time systems, operating systems
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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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, the project PSYBER - Assessing Cybersecurity Preparedness will be executed closely with Prof. Marcus Völp (Robustness and Resilience in Computing), Prof. Pedro Cardoso-Leite (Faculty of Social Science
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As a part of this collaborative research programme, you will join as one of three PhD candidates working on interconnected projects in Quantum Optimisation, People-Centred Design (PCD
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an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social
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-creating an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and
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The SigCom research group of SnT, headed by Prof. Symeon Chatzinotas, focuses on wireless/satellite communications and networking. The research areas focus on the formulation, modeling, design, and analysis of future 6G communication networks that are capable of supporting new services for...
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: Socio-spatial implications of transformative practices”) is a Doctoral Network funded by Marie Skłodowska-Curie Actions (Horizon Europe Programme). TRANSFORM investigates the efficacy of transformative
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid