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on advanced statistical methods for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors contribute to severe brain
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in supervising students at both master's and PhD levels. This project is funded by a prestigious FRIPRO Grant for Early Career Scientists and is built on our ongoing Integreat project “Embedded
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at the Faculty with a view to obtaining the degree of PhD. The successful candidate is expected to join the existing research environment/network of the department and contribute to its development. Read more
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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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care sectors and employs qualitative and ethnographic research methods. About the two positions The two successful applicants will form a research team in the WAGE project alongside project leader Ines
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PhD to be part of the Nordic collaborative project PreciMENT - Leveraging Nordic Opportunities to Transform Mental Health Care through Precision Medicine. The overall goal of the project is to transform
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
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advanced computational methods. Knowledge of c++ program-ming language is particularly desirable Experience with supervising Bachelor/Master/PhD students Strong track record relative to the career stage
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dissecting the functionality of different EV subsets. Methods include, but are not limited to, in vitro culture of primary human cells and cell lines, organ-on-chip models, molecular biology, diverse
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calculations. The focus of the project is to perform lifetimes measurements of excited states ranging from few picoseconds to several microseconds via recoil distance Doppler shift (RDDS) and fast timing methods