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) Familiarity with mathematical programming solvers (e.g., Gurobi, CPLEX) or probabilistic/Bayesian computing frameworks (e.g., Stan, PyMC) Ability to formulate high-impact, novel and well-defined research
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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skills. Experience in probabilistic analysis and uncertainty quantification within an engineering context, a track record of high-quality peer-reviewed research, and the ability to work independently and
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processes under different biological conditions. Apply statistical learning, deep learning and probabilistic modelling approaches to large-scale cancer datasets. Evaluate and benchmark computational methods
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on a project in the area of power system risk modeling and assessment. Key Responsibilities: Develop a probabilistic security assessment framework for heterogeneous operational uncertainties and
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attention mechanisms Strong background in probabilistic machine learning Proven track record in time-series analysis and modeling, signal processing, medical domain, and/or related fields Strong writing
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populations in the context of nanohertz gravitational-wave observations Applying probabilistic inference to gravitational waves, including through machine learning techniques Analyse and interpret research
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-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater