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Field
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programme spans four years, beginning with one pre-PhD year followed by a three-year PhD project. Programme highlights – What’s in it for you… Three lab rotations in excellent neuroscience labs during the pre
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implementation Knowledge of networking protocols and architectures PhD or equivalent R&D experience in computer science, networks or communications, and/or experience with at least one of the following areas
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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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, supported by access to rich datasets, computing resources, and collaborative networks. The position offers opportunities to contribute to impactful projects, mentor students, and develop research portfolios
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, to develop a novel end-to-end neuromorphic design approach based on spiking neural networks (SNNs). The project aims to develop novel computing solutions for the defence and security sector, that can
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We are seeking applicants for a PhD Fellowship in the framework of the MSCA Doctoral Network MASAUTO: MAterials for Smarter AUTOnomous sensors. MASAUTO is a research and training program for 12
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infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in
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. The PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en/research/phd-programmes/phd-programme-in
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candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields shown below. The candidate is expected to have hands-on experience in
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, theoretical analysis etc.), running experiments, and writing articles for publication in tier-1 journals and ML conferences. Job Requirements: PhD in Mathematics/Computer Science/Statistics or related fields