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The Department of Mathematics and Mathematical Statistics is opening a PhD position in mathematics, within the WASP AI program, focusing on geometric deep learning. The position covers four years
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systems and statistical mechanics as well as skills in molecular dynamics simulations are highly desirable especially for the first position. Proficiency in programming would be beneficial. A good command
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the CMGG lab: independently perform the genetic analyses. Data processing and analysis: structuring and processing the results obtained. Statistical knowledge is required (data mining, hierarchical cluster
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percussion. Morphometric data on bladelet obtained using geometric morphometry tools will be statistically processed. This novel aspect will be the subject of collaboration with V. Bonhomme and A. Evin (UMR
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department of Mathematics and Mathematical Statistics is opening a PhD position in mathematics, within
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are looking for an enthusiastic colleague who meets multiple of the following requirements: You have (completed or almost completed) a Research Master’s degree in AI, NLP, Statistics, Data Science, Computer
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Master’s degree in Psychology, Sociology, Methods & Statistics, Organisation Science, Economics, Management, Public Administration, or a similar study. You have the ambition to conduct excellent scientific
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; Midlands Graduate School Doctoral Training Partnership | Birmingham, England | United Kingdom | 1 day ago
, including economics, business and management studies, innovation, internationalisation, social statistics and applied mathematics. A master's degree is not mandatory as comprehensive postgraduate training
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"with memory" are of importance in several disciplines, such as for financial and statistical modelling. However, due to the correlation structure their computational simulation is very challenging
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learning meets choice models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent advancements have improved its economic