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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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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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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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interfaces to enable their exploratory and computational analysis. Conduct original data-driven historical research on newspaper and radio sources Contribute to the development of innovative computational
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The successful candidate will join the Enzymology & Metabolism group, led by Prof. Carole Linster, which deciphers the cellular metabolism in rare and more common neurodegenerative or neurometabolic diseases. We exploit genomic and post-genomic data to fill gaps in metabolic networks, discover...
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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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Education:MSc degree or equivalent in Computer Science or Engineering Experience: The ideal candidate should have some knowledge and/or experience in several of the following key topics: Energy
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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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Please prepare the following documents in English: Curriculum Vitae (studies with grades, work experience, publication list) Cover letter Abstract of your thesis List of 3 referees description of possible research interests/ideas Early application is highly encouraged, as the applications will...
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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