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internationally Remuneration and further information The position is placed in position code 1017 PhD. Fellow (NOK 550800). For particularly well‑qualified applicants, a higher salary may be considered. Seniority
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and analytic structures for the analysis and modelling of complex sequential data. Path signatures, originating in stochastic integration and rough path theory, provide expressive representations
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publishing. Knowledge and experience of machine learning, preferably distributed machine learning. Knowledge and experience of coding and information theory. Knowledge and experience of communication networks
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well-founded conclusions Be flexible and open to adjusting the plan for the project as needed Combine theory and practice using industry-as-laboratory approaches. Emphasis will be placed on personal
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. The student will be responsible for organizing, improving and participating in teaching in optical microscopy and assisting in theory exercises in mineralogy for course TGB4126 Mineralogy, corresponding
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and Technology (NTNU) for general criteria for the position. Preferred selection criteria Knowledge of plasticity theory. Knowledge of constitutive modelling of materials. Knowledge of non-linear finite
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environment in quantum condensed matter theory and experiments, embedded in the Quantum Condensed Matter section of the Department of Physics, which hosts a Center of Excellence in Quantum Spintronics. Please
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theory and application, providing insights that could redefine how we predict and enhance the performance of aluminium alloys under extreme conditions. If you are passionate about materials science
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processing data streams, with theories and methods for planning and logistics. The PhD research will focus on AI-enhanced planning of operations in shipbuilding supply chains, examining how AI techniques can
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will utilize economic theory, simulation, economic evaluation and machine learning to quantify the benefits of advanced diagnostic technologies in reducing overdiagnosis. Competence You must have