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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context are differentially private algorithms for statistical model
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and/or research experience in one or several of the following areas: methods and models related to power system protection and control or real-time systems and power system automation studies
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on childhood dementia CLN3, as part of ongoing research in the Bjørås group . About the project The current project is aiming at developing novel human models for childhood dementia CLN3 to recapitulate disease
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Hardware-in-the-loop testing) and control-in-the-loop testing will be an advantage. Experience with stability studies based on state-state modeling of power systems will be merited. Personal characteristics
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are differentially private algorithms for statistical model parameter estimation under different trust relations. About the project The position is funded by the Norwegian Research Center for AI Innovation and will be
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data analysis workflow from data import/cleaning to descriptive statistics and at least one inferential model. Code must be included and clearly commented. This is required to demonstrate advanced
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expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently
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knowledge about natural resource management Knowledge of software R Strong skills and/or interest in mathematical and statistical modelling is a strength Ability to conduct field work in remote alpine areas
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with—or documented interest in—advanced statistical methods for causal inference in observational data, model-based imputation, genetic epidemiology, or the application of machine learning to registry
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, model-based imputation, genetic epidemiology, or the application of machine learning to registry data is highly valued. Clinical experience with patients suffering from headache disorders is considered