571 algorithms-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" Fellowship positions
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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) Design robust obstacle avoidance algorithms for mobile robots in dynamically changing environments, focusing on formal safety constraints and real-time performance in unpredictable conditions. b) Develop
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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positions, at NTNU – Norwegian University of Science and Technology, USN - University of South-Eastern Norway, and UniCA – university of Cagliari. There will also be several associated PhDs from closely
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on high-speed vision perception for autonomous driving. This project aims to advance the state of the art in visual perception algorithms and real-time systems for autonomous racing, pushing the boundaries
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operation. Develop modular architectures for multi-agent coordination, sensing, and communication. Integrate sensor suites, flight controllers, and swarm coordination algorithms into UAV platforms. Conduct
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of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas: image processing, machine learning, and patient records. Track
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing