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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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behavior. Includes expertise in TCP, HTTP, DNS, DHCP. Ability to analyze network performance through ongoing monitoring and using troubleshooting tools. Recommend enhancements, upgrades and changes
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, consultative and interpersonal skills, including oral, written and presentation. Must mechanical skills, basic computer skills, basic understanding of UPS and PDU equipment, basic understanding of data network
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of user support services, including network support, training, and computer desktop support. This role involves end-user support and training, focusing on endpoint technologies (desktops, laptops
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Deadline 17 Apr 2026 - 17:00 (Europe/London) Country Ireland Type of Contract Temporary Job Status Full-time Hours Per Week 39 Is the job funded through the EU Research Framework Programme? Not funded by a
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, physics or a similar area - very good programming skills in Python - good prior experience with neural networks using common Python-ML libraries such as PyTorch - background knowledge in computational
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TASKS The Marchal lab (IDLab/IMEC Ghent University, https://idlab.ugent.be/people/802000961346 ) has an open PhD position within the Marie Sklodowska-Curie Doctoral Networks (MSCA-DN) project iSTRIDE
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 16 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python
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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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Related works: [1] Sagawa et al., Distributionally Robust Neural Networks for Group Shifts (GroupDRO) https://arxiv.org/pdf/1911.08731 [2] Liu et al., Just Train Twice: Improving Group Robustness Without