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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Personal
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Digital Twin for façade condition, fire safety risk classification, and maintenance planning Apply statistical and machine-learning methods to link climatic loads to degradation indicators Validate models
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relevant if there is a strong focus on data-driven modeling, machine learning, and control. In any case, a documented background or experience in control is required. Your education must correspond to a five
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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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) for general criteria for the position. Preferred selection criteria Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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academic writing (e.g., previous conference or journal articles) Personal qualities Be highly motivated for completing a PhD Be open-minded and eager to learn Be goal-oriented, accurate, analytical and
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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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Computer science » Computer systems Computer science » Programming Technology » Communication technology Technology » Telecommunications technology Researcher Profile First Stage Researcher (R1) Positions PhD
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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