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maintaining a strong commitment to a healthy work-life balance for our employees. Moreover, equality, inclusion, and diversity are core values of our organization. About the position Forecasting techniques
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, GenAI models act as probabilistic black boxes, often producing plausible but factually incorrect information without source attribution. This poses unacceptable risks particularly in high-stakes domains
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understanding the dynamic processes behind uncertainty quantification of offshore wind forecasts. Understanding the spatial uncertainty information needed for real-time grid management, market integration, and
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of Stavanger (UoS). The position is funded within the project “SURF: “Subsurface Understanding for Robust emissions Forecasting”. SURF is funded by the Research Council of Norway and industry partners. We
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communication skills in English Desired qualifications: Experience with transformer models and attention mechanisms Strong background in probabilistic machine learning Proven track record in time-series analysis
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probabilistic behavioral models for verification, performance evaluation, and optimization using model-checking techniques, ultimately bridging static system design and dynamic operational analysis. We offer
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. 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 models that forecast
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initiative aimed at reframing how we forecast and assess the capabilities and risks of advanced AI systems. The purpose of a postdoctoral position is to build up a researcher profile that qualifies for a