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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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that emerge in AI-augmented teams and the leadership practices that shape them. It should also deliver validated measures and manager-facing workshops or tools that help teams notice weak scrutiny, protect
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microscopy and SEM, with computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties
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should also deliver validated measures and manager-facing workshops or tools that help teams notice weak scrutiny, protect critical thinking and use delegation without hollowing out collective learning
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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benefit-assessment tool for selected TrainGate cases. It will compare detection and intervention scenarios and support maintenance decisions, investment assessments and implementation, subject to available
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on infrastructure degradation, maintenance needs, operational risk, punctuality and costs. The aim is to develop and validate a practical, transparent benefit-assessment tool for selected TrainGate cases. It will
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the position We
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understand how AI can be used safely and reliably in decision-support tools for the energy sector. The enhanced framework will be tested on Nordic hydropower use cases, including long-term and medium-term