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projects of the MQS group. Our recent research has focused on the theory and applications of variational quantum algorithms and quantum machine learning. We also have activity in quantum optics, so
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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mechanisms and kinetics to stabilize highly active but metastable surface motifs sustainable catalytic processes. Modeling Atomic Processes on Nanoparticles Develop atomistic models and machine-learning
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with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order manufacturing. This can be proven by the attendance
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Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI
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intelligence, and research software engineering, and is interested in developing robust, transparent, and sustainable computational tools. Experience with first-principles electronic structure methods, machine
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design