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. This position will play a key role in advancing this work. We are seeking project proposals from all genres and musical practices where artificial intelligence is a central component of the artistic work. Scope
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Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis
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you have started work in the position. The candidate is required to complete the mandatory joint training component in the National Research School in Artistic Research . Qualifications and personal
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of the research training must be approved by the faculty within three months after you have started work in the position. The candidate is required to complete the mandatory joint training component in the National
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by the faculty within three months after you have started work in the position. The candidate is required to complete the mandatory joint training component in the National Research School in Artistic
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period of 4 years. There is a 25% component of the position that is devoted to teaching and administrative duties / other career-promoting work. These duties also include obtaining basic teaching
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for patient-derived tumouroids, including experimental design, pilot studies and SOPs. Computational work in building and maintaining robust analysis pipelines for high-content image data (e.g. CellProfiler
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robust analysis pipelines for high-content image data (e.g. CellProfiler / deep learning workflows) and integrate these readouts with viability metrics and genomic data. Collaborate closely with clinicians
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ability to contribute to more than one work package, this is counted as a positive. ● Area1 - Lie Theory: Analysis and Geometry of Nonholonomic Systems Areas of Research: Sub-Riemannian geometry and Cartan
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
background in one or more of the fields of rock physics, petrophysics, seismic attribute analysis, seismic inversion (both pre- and post-stack inversions), and machine learning with geoscience knowledge