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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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candidate with a background in SAR/InSAR signal processing and time series algorithms, combined with strong expertise from an application domain that strengthens the group’s current activities. Key
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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project aims to develop advanced control and planning algorithms that enhance robustness and safety, ensuring reliable performance even in the presence of magnetic fields and other uncertain conditions. The
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UiA-CERN PhD Position in Multi-robot Mapping and Environmental Data Sharing - Uncertain Environments
representation, efficient transmission strategies tailored to mission requirements, and algorithms for combining data from multiple sources to improve accuracy and visibility. The project will also explore
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environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software
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intelligence over the next five years SURE-AI is a Norwegian AI centre funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms for inference and
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, finger, and multimodal) under controlled and semi-wild conditions. Develop AI-based algorithms for biometric trust assessment, anti-fraud analytics, and secure onboarding. Lead the deployment and
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods