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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
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well as several associated partners (https://www.nnrc.uio.no/english/research-themes/rt-5/ ). The PhD candidate will be part of a dynamic and productive academic team with a large international network covering
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) is entitled Multiscale and multimodal spectral image acquisition, integration, analysis and visualization. Objectives: This project will develop strategies for the acquisition, integration, analysis
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position A new PhD fellowship in path signatures, stochastic analysis, and learning from
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, Department of Physiology Analysis of biological and experimental data Preparing manuscripts that meet the requirements for publication in international journals and compile them into a doctoral thesis in
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techniques for studying dough and bread characteristics (e.g., rheology and texture analysis) Heat and mass transfer Mixing technology Statistical analysis Personal qualities: Inquisitive and driven
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experiences and skills will be emphasized: Experience in plant phenotyping, drought stress research, or root biology Familiarity with imaging technologies, statistical analysis, or quantitative data analysis
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selection criteria Knowledge of quantum communication / quantum information fundamentals (e.g., entanglement, fidelity/noise, quantum repeaters) Experience with network modelling, performance analysis
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. Qualifications considered an advantage Experience with fermentation, starter cultures, or food processing Experience with proteomics, omics data, or bioinformatics. Experience in food analysis or microbial
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. AI-based performance assessment in early design, integrating rapid analysis tools, multi-criteria performance estimation, and surrogate modeling. Human–AI collaboration in design, including agent-based
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interpretable framework for tensor analysis. Specifically, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for