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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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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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conferences and contributing to collaboration between NIBIO, NMBU, and national and international research partners. Professional qualifications (required) A Master’s degree in machine learning, artificial
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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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machine learning and advanced analytical approaches Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Show curiosity and a strong motivation for the subject
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results into useful decision support Publish and communicate results and
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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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of Computer Science, Norwegian University of Science and Technology (NTNU). The position offers the opportunity to work on cutting-edge research at the intersection of deep learning and computer systems. The successful
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Machine Learning, Reinforcement Learning, AI-based time-series forecasting English language skills, both written and spoken, corresponding to the scale C1 in the Common European Framework of Reference