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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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distribution of star-forming carbon monoxide around Cosmic Noon (targeting redshifts between z~2–3) and the Epoch of Reionization (z~6–8). Currently, the experiment is in the COMAP-Pathfinder phase, which
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targets for use in laser fusion experiments, to increase, and improve the target ignition and utilization. In addition to developing and fabricating the targets, a part of the project will be to build a
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Norwegian national Laser fusion project FUSENOW , supported by the Trond Mohn Research Foundation. About the project/work tasks: The project aims to design, develop and characterize nano-fabricated targets
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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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expected to apply for a planned 25% position at the project partner Vianode in Kristiansand. In this targeted setup the Ph.D. position at the University of Agder would be adjusted to a 4 year 75% position
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this targeted setup the Ph.D. position at the University of Agder would be adjusted to a 4 year 75% position to make a total 100% position over 4 years. The starting date is January 1st 2026 or to be negotiated
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Kristiansand. In this targeted setup the Ph.D. position at the University of Agder would be adjusted to a 4 year 75% position to make a total 100% position over 4 years. The starting date is January 1st 2026
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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