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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
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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
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24th April 2026 Languages English English English The Department of Mathematical Sciences has a vacancy for a PhD Candidate in Mathematical Foundations of Machine Learning for Sequential Data Apply
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position Distributed machine learning takes advantage of communication and
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. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design
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Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Experience with predictive maintenance, fatigue, fault detection
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interconnected Internet-of-Energy (IoE) ecosystems. In this context, the MSCA Doctoral Network project SAILING (https://Secure AI and Digital Twin Empowered Smart Internet-of-Energy ) aims to establish a
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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the NordForsk project ‘RISK-AI’ 230778. https://www.nordforsk.org/projects/responsible-innovation-and-social-knowledge-artificial-intelligence-risk-ai and is based at the Department of Computer Science in