117 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" PhD scholarships in Norway
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- NTNU - Norwegian University of Science and Technology
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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for machine learning models to optimise membrane properties, structure, and fabrication. The fellow will play a key role in the experimental part of the project, including: Preparation and characterisation
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is
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-year master’s degree, with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented strong academic background
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information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/292175/phd-position-on-the-history-of-medical-plastics Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/292175
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are particularly suitable for a PhD education. You must meet the requirements for admission to the doctoral program from the Faculty of Engineering (https://www.ntnu.edu/studies/phiv ) You must be
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techniques to identify the enzymes, as well as protein purification and recombinant protein expression to get hold of the enzymes. The PhD student will be part of the large and international PEP group (https
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maintaining creative autonomy and avoiding the risk of machine-driven homogenisation? What are the cultural, ethical, and market implications for artists and audiences when AI systems integrate into creative