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models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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are looking for candidates with a technical university degree in, for example, mechanical engineering, computer science, manufacturing engineering, or another relevant field. Strong analytical and communication
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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-exploiting optimization algorithms will be used to improve the performance of the numerical methods also for this class of problems. As postdoc, you will principally carry out research. A certain amount of
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, C/C++, Java. JavaScript), especially in design, analysis and implementation of geometric algorithms (computational geometry, map-based web interfaces, GIS). It will be considered a merit if you also
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is