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complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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competence in seismic data analysis is a requirement. Experience from or competence in computer programming (MATLAB, Python) is an advantage. Applicants must be able to work independently and in a structured
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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a requirement. Experience from or competence in computer programming (MATLAB, Python) is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good
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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel
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learning-based image classification approaches. The objective is to quantify landscape changes over decadal timescales, with a particular emphasis on Western Norway. Relevant transformations include
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national and international partners. The PhD project will focus on integrating advanced photogrammetric techniques applied to historical aerial imagery with modern deep learning-based image classification
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in Norwegian (or another Scandinavian language) is an asset, but not a requirement. The successful applicant must, however, be able to teach in Norwegian within four years of his/her appointment