160 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" PhD scholarships in Norway
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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
- University of Oslo
- University of Bergen
- UiT The Arctic University of Norway
- Molde University College
- OsloMet
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
- OsloMet – Oslo Metropolitan University
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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/ ). By combining advanced machine learning techniques with qualitative methods, the project will investigate usage patterns and engagement levels with a health app across multiple European countries
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/ ). By combining advanced machine learning techniques with qualitative methods, the project will investigate usage patterns and engagement levels with a health app across multiple European countries
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. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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seeking a PhD candidate in computer architecture. The research focus of this PhD position is the design of performant and energy-efficient Edge Artificial Intelligence (AI) accelerators. Such accelerators
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. You will become part of a dynamic, collaborative working environment with expertise in drilling engineering, geomechanics, machine learning, and energy systems. The project will integrate real‑time
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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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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract