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image analysis, data processing and machine-learning-based modelling. More about the position The main purpose of the fellowship is research training leading to the successful completion of a PhD degree
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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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distribution in regenerative biomaterials. The candidate will receive interdisciplinary training in biomaterials research, advanced imaging, quantitative image analysis, data processing and machine-learning
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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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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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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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of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
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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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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning