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- NTNU - Norwegian University of Science and Technology
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(Python, C++, etc.). Experience in mathematical programming and/or optimization software such Pyomo, GAMS or similar. Proficiency in process simulation software (Aspen Plus, Aspen HYSYS, etc.). Experience
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background and research-oriented master thesis in a related field (e.g., signal processing, statistical machine learning, applied mathematics); Significant experience with programming (preferably Python). Good
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that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program(https://www.ntnu.edu/studies/phiv ) Very good English skills (both
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validating mechatronic systems. Experimental methods and testing Programming and software development, specifically with Python-based engineering software development and hardware interfacing. Excellent oral
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must have a solid background in mathematics or physics, and good programming skills (e.g. Python or MATLAB). You must meet the requirements for admission to the Faculty's PhD programme in Electronics and
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, longitudinal modelling, machine learning and multivariate approaches. Proficiency in programming (e.g., MATLAB, Python, or R), handling large datasets, and working with complex analysis 2 pipelines is an
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
work. Some experience in modelling of composite materials. Some experience in programming (Python, Fortran, CUDA or similar). Some experience in fluid mechanics and biomechanics. Personal characteristics
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environmental conditions. The PhD position is funded through the Norwegian Nuclear Research Centre (NNRC). Link: https://www.nnrc.uio.no/english/index.html The PhD candidate will be part of a dynamic and
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. The research team consisting of eight faculty members, the Industrial Ecology Digital Laboratory, and about 70 researchers, post doctors and PhD students. More information can be found at https