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energy imbalance (EEI) and ocean heat content (OHC), in order to close an existing knowledge gap and im-prove near-term predictions. Other partners in the project are University of Ber-gen and Nansen
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page to watch video, or click here to open video) About the position The position is part of the research project “Prediction of genetic values and adaptive potential in the wild (GPWILD)” (https
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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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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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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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, e.g. HPLC, ICP-MS. Experience with using nuclear-reaction codes which are commonly used to predict the reaction cross sections for medical isotope production, e.g. TALYS, TENDL, CoH, ALICE and EMPIRE