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knowledge of syntax-based statistical analysis tools. Strong skills in developing reproducible and transparent analysis workflows. Solid background in machine learning and analysis of large and complex
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. Its primary objectives are to: Improve understanding of intra-Draupne sandbodies to support more reliable reservoir modelling. Characterize reservoirs and seals. Develop innovative exploration
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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aim to increase the relevance and adoption of the solutions identified. The work will also allow for opportunity to develop fundamental understanding of the drivers of landscape multifunctionality, e.g
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ability to develop the candidate, relevant publication activity Visits to international institutions for periods of 6-12 months will generally be valued positively. The application should include a
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to increase the relevance and adoption of the solutions identified. The work will also allow for opportunity to develop fundamental understanding of the drivers of landscape multifunctionality, e.g. theory
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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department are obligated to facilitate the further development of the appointee’s qualifications in teaching, research, dissemination, and innovation. To be considered qualified, applicants must have obtained
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internationally. Our collaborators include leading research groups in the UK, Denmark, and Spain – including the world-renowned High-Dimensional Neurology Group at UCL Queen Square Institute of Neurology in London
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borehole electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed