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application process here. About the position The Department of Information Security and Communication Technology invites applications for a fully funded PhD position on AI-driven network operations for cloud
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of Information Security and Communication Technology invites applications for a fully funded PhD position on AI-driven network operations for cloud-native 5G/6G networks. The project will explore the mechanisms
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. The PhD candidates will focus on developing methods that utilize remote sensing and AI supporting precision forestry. Remotely sensed data, such as images, lidar, and photogrammetric point clouds acquired
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to move key AI applications from Cloud datacenters to Edge devices. In addition to reducing demand for datacenters, Edge AI also has the potential to improve upon Cloud AI by reducing latency and
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(i.e., an SFI ) in which NTNU, the University of Oslo, and key industry players work together towards developing the knowledge and technology required to move key AI applications from Cloud datacenters
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for scientific data acquisition (e.g., mapping missions). Background knowledge in drone or satellite-based LiDAR remote sensing. Experience in workflows for drone data processing (e.g., Agisoft Metashape, Cloud
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UiA-CERN PhD Position in Multi-robot Mapping and Environmental Data Sharing - Uncertain Environments
aims to design and implement a cloud-based architecture for storing and managing maps and associated sensor data, enabling data sharing across multiple robots and missions. It will focus on compact data
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properties. Optical systems may provide excellent resolution on a cloud-free day. Synthetic aperture radars (SAR) deliver image products even through clouds, but they are expensive. Our main research question
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systems may provide excellent resolution on a cloud-free day. Synthetic aperture radars (SAR) deliver image products even through clouds, but they are expensive. Our main research question is: How can small
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detailed imagery, but only under cloudfree conditions. Synthetic aperture radar (SAR) can deliver highquality images regardless of cloud cover, but such systems are expensive. Our main research question is