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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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the chips and demonstrate the capabilities of the PICs. The PhD will collaborate with researchers in machine learning for analysis of the recorded Raman spectra and with biologists on the utility
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Norway, NTNU will arrange for you to acquire such competence during the employment period. In such cases, you will also be assigned relevant teaching as part of the career-promoting work. The appointment
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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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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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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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-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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or Machine Learning). The Master’s thesis must be included in the application. Ideal Candidate: Demonstrates experience or strong interest in modelling, programming, systems thinking, and qualitative
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, Mathematics (Operations research) or Computer Science or Machine Learning). The Master’s thesis must be included in the application. Ideal Candidate: Demonstrates experience or strong interest in modelling