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Field
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The postdoctoral fellow will work at the interface of epidemiology, causal inference, prediction modelling, responsible AI, digital health and global maternal and child health. The work will include development and
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interactions. This involves (i) developing predictive machine learning models that forecast user actions and remote system responses across audio, video and haptic modalities, and (ii) jointly orchestrating
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products. At NTNU, a central part of the project is the development of more predictable and efficient methods for the refactoring and heterologous expression of biosynthetic gene clusters (BGCs
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soft materials. Be directly involved in integrating experiments and theoretical prediction. Develop a new understanding of the fundamental flow physics through theoretical and/or numerical work. Develop
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data sources - such as AIS, metocean, emissions, port, cargo, and business data - to improve predictions of costs, freight rates, delays, emissions, and port logistics. While strongly rooted in real
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an aerodynamic bird collision avoidance model, combining computational fluid dynamics (CFD) of the flow around wind turbines with the aerodynamic characteristics of flying birds to predict collision risk. This
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well as resource limitations. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning
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ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently
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and limitations arising from the use of AI-based methods in predictive feedback. The successful candidate will: explore how a combination of multimodal observation (audio, video, LIDAR, thermal vision
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refinement. Accurate wind simulation at multiple scales helps in better predicting energy production and reducing operational risks. Some relevant key words (see FME-NorthWind webpage for more details