158 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Norway
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to
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-FATES model using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently
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for Knowledge-driven Machine Learning. We are looking for a motivated researcher, who has experience with both theoretical, methodological and applied research in change and anomaly detection in sequential data
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of this position is to develop a massively parallel version of a computer code called Commander, and apply this to archival data from Planck HFI, new data from Simons Observatory, and simulated data from LiteBIRD, a
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engage with previous empirical literature on iconicity and iterated learning experiments. The candidate will help train the PhD candidate and train and supervise research assistants. The position is
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of the post-doctoral position is to qualify for work in high-level scientific positions. A PhD degree is required to work on the following tasks: Conduct advanced research on contaminant removal techniques in
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one Postdoctoral Research Fellowship at the University of Oslo. Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency in the course