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molecular simulation techniques, specifically density functional theory (DFT) and molecular dynamics (MD) simulations. You are keen to learn new techniques for the development of machine learning interatomic
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, vegetation occlusion, and low illumination conditions; - Contribute to the definition of resilient perception architectures for outdoor robotic operation. 2. Machine learning-based resilient perception
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) simulations. You are keen to learn new techniques for the development of machine learning interatomic potentials for MD simulations. You have a strong academic track record, as evident from your Master's thesis
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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microscopy and SEM, with computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties
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UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the position We
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University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
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try again. UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic