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and time. In this project we will model the ionosphere-ground system with a unique time-domain model which is developed in the group for ’Solar-Terrestrial Physics and Space Weather’ at the Department
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, Properties and Units), ICD (International Classification of Diseases), or similar. Experience in statistics, epidemiology, and/or validation of computational models. Clinical expertise as a medical doctor with
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or post- transcriptomic mechanisms of plant plastic response to stress using wild Arabidopsis thaliana ecotypes as a model. The successful candidate will have the opportunity to work on a cutting-edge
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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barrier in the planned KBS-3 concept for final disposal of spent nuclear fuel. The PhD student will develop, apply, and combine theoretical molecular dynamics (MD) simulations with experimental techniques
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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models is a merit. Candidates are expected to have a genuine interest in computer security, and are required to have very good knowledge of programming (such as in C, Java, or Python). Another requirement
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are of interest. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial
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has gone through a very rapid development in the last few years. Large-scale machine learning models are however notoriously over-confident. With insufficient amounts of data to train them on together