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Field
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT
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optimization strategies to maximize yields of desired products. A key aspect of the project is understanding and improving catalyst stability, including identifying deactivation mechanisms and developing
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learning, with the aim of controlling the vessel in an optimal and efficient manner. In this context, control is formulated as a trade-off between different objectives, such as minimising energy consumption
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-MID prototyping and performance optimization of structure-embedded sensors and nanogenerators. Depending on applicant’s skillset the research objectives may include implementation of AI-assisted in
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, by that enabling improved control and optimization. Stall detection will be a natural part. The PhD project is internally financed but will be associated to two large research programmes at NTNU and
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, and stay abreast of the latest advancements in OLED display technology. He/she will oversee R&D activities related to OLED emitters and devices, providing technical guidance and leadership to optimize
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with advanced photoreactors, modular LED light sources, inline analytics, and machine-learning algorithms for the autonomous optimization of photocatalytic reactions. Research will address key challenges
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
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brings together expertise in energy informatics, optimization, data science, social sciences, and governance to develop trusted data-sharing solutions for the heat transition. Within the project, you will
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their