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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
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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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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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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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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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experts in machine learning, classical algorithms, and many-body quantum systems, with research topics including: Adiabatic and measurement-based quantum computing, Tensor networks for machine learning
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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