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scientific programming (Python) and deep learning frameworks. Fluent oral and written communication skills in English. Desired qualifications: Expertise in broader topics in computer science, information
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for appointment. Solid background in machine learning and scientific profile relevant to the project described above. Proficiency in scientific programming (Python) and deep learning frameworks. Fluent oral and
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. The plan is for the candidate tofocus on Bayesian modelling in close collabora-tion with researchers at the HISP centre that work on complementary deep learning approaches to disease modelling as
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at the HISP centre that work on complementary deep learning approaches to disease modelling as well as on development of plat-forms for running and evaluating prediction models. The PhD candidate will develop
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entails teaching responsibilities within automation of synthetic chemistry and high-throughput experimentation. We seek candidates who demonstrate a collegial approach to teaching and a deep appreciation
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candidates who demonstrate a collegial approach to teaching and a deep appreciation for the societal role of university education. As a matter of strategic decision, Department of Chemistry has acquired a
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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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