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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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command of written and spoken English • Experience with qualitative research methods is an asset • Good knowledge of machine learning /data mining in science • Good programming skills in at least one
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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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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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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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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer architecture Computer science » Computer hardware Researcher
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Mainmenu Information for Prospective Students Current Students Staff Teaching Staff Alumni Media Business Lifelong learning Quicklinks All Degree Programs ALMA Portal Excellence Strategy Staff Search (EPV
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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