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Federated Cloud Computing ( HCC ) We invite applications for a fully-funded 3-year PhD position on the topic of usable decentralization, i.e. on making distributed and federated cloud services accessible
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computational challenges. This PhD project will investigate novel registration strategies for ultrasound-derived musculoskeletal point clouds. The work will focus on developing geometry-aware alignment methods
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bring strong research expertise and strong technical skills within remote sensing and ecology, notably lidar processing. The successful candidate should have: Required: A PhD degree with a publication
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the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress
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appointment in the lab. You will report to the Professor Marina Romero-Ramos Your competences We are seeking a highly motivated and independent postdoctoral researcher with a background/PhD in computational
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for intelligent human-robot interaction that can provide personalized support without requiring continuous cloud connectivity, collaborating closely with the second PhD student to ensure technical solutions address
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areas: cyber security privacy engineering cryptography and applied cryptography computer engineering edge or cloud computing and networking. You will be part of one of the department’s research groups in
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distribution infrastructures, and heterogeneous networks and storage environments based on Cloud based services, CDNs and Edge Computing. This complex chain of delivery has negative implications
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hardware; optimization techniques for high-performance multicore processors; innovative hardware solutions targeting cybersecurity resilience; leveraging AI and cloud technologies in EDA; edge computing and
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interdisciplinary research and dialogue. Qualifications Successful applicants must: Hold a PhD or similar qualifications within relevant fields of Computer Science or Mathematics. Exhibit curiosity-driven research