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Metal additive manufacturing process monitoring and control – Researcher, PhD position (ERC project)
of process condition variations. The important parts of the control system to be developed within this project are i) coaxial measuring of meltpool depth variations, and ii) machine learning-based models
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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additive manufacturing or other laser-metal machining processes. Where to apply E-mail dominik.kozjek@fs.uni-lj.si Requirements Research FieldEngineering » Mechanical engineeringEducation LevelPhD
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of the employee will be the development of large machine learning models (so-called foundation models) for the analysis of sensor data. These are general-purpose models that can be adapted to various applications
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or development of machine learning methods, or a desire to learn these skills, are also welcome. We offer the opportunity to work on interesting scientific challenges using modern experimental methods available in
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or development of machine learning methods, or a desire to learn these skills, are also welcome. We offer the opportunity to work on interesting scientific challenges using modern experimental methods available in
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enrollment: University of Ljubljana Duration: 36 months Supervisors: A. Žnidarič, A. Anžlin (ZAG), M. Skobir (CES) Objectives: Leverage vision-based monitoring and machine learning (ML) to enable enforcement