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Field
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Drive cutting-edge AI research in traffic video analytics, developing and deploying state-of-the-art computer vision models for real-world impact. Full-time appointment on a fixed term basis for two
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About the Opportunity About the Institute Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions? We are launching a
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to violent). Machine learning techniques will then be used to find a global functional parameter set, from the single-column experiment results, that offers the best performance for the model redistribution
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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transfer function Apply machine learning methods to identify optimal model parameters that can be used in large-scale sea ice simulations (either global parameters, or as a function of existing model
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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techniques, or tight-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning
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Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing and Image Analysis as part of Visual Intelligence , Norway’s leading research centre