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application! We are looking for a postdoctoral researcher to work on the fundamentals of knowledge graphs and virtual data integration. Work assignments You will actively participate and lead work tasks in two
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measure theory. You are encouraged to comment on how your research relates to these areas. Of merit is also research within other fields of mathematical analysis, experience in applying for and obtaining
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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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image analysis, deep learning as well as mathematics. You have substantial expertise in programming, especially in Python and Matlab. You are independent, meticulous and work efficiently. Since
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synthesis, material science, theory and modeling, device physics, nanotechnology, biotechnology, and system design. Our activities span the range from basic research to commercialization, the latter carried
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in applied mathematics
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of visualizations and presentations of research findings. The person we need You have graduated at Bachelor’s or Master’s level in machine learning, computer science, mathematics, physics, or a related area that is
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you have graduated at Master’s level in Natural Sciences or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in Mathematics or Statistics. Alternatively