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used in analysing drivers of species communities and to make predictions to non-explored areas. In this project, these models will be extended to predict ecosystem functions (e.g., total biomass
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, assessing their impact on planetary environments, and validating the model results with in-situ measurements. Requirements MSc (or equivalent) in space physics, astrophysics, or a related discipline completed
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into an expert in magnetic components in modern power electronic systems. The following topic with international secondments is offered: Design of inductors using time-domain loss models Objective: Design and
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the "Mercury in the solar wind" ERC project at the Finnish Meteorological Institute. The PhD student will apply our global particle-based models to study the solar wind influence on Mercury and its environment
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stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven
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, Psychology or a related field excellent knowledge in AI and at least one of these three key areas: quantitative user studies, statistical modelling of human experimental data or human augmentation very good in
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at adapting our blood-brain barrier model to predict the brain permeability of extracellular vesicles and therapeutic agents. It by the use of cell culture, automation, high content imaging, TEER and
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
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. The handling of unseen languages remains without real solutions, despite the research community having made a few practical excursions, such as using trash language models or confidence thresholds