10 research-group-on-applied-mathematics-and-modeling PhD positions at Linköping University
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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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application! We are looking for a PhD student in automatic control at the Department of Electrical Engineering (ISY). Your work assignments The research area for the position is complex networks and multi-agent
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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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medicine research is expected to make use of existing strong assets in Sweden and abroad, such as molecular data (e.g. omics), imaging, electronic health care records, longitudinal patient and population
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of complex systems in engineering and medicine. Your research will bridge this gap, and it will be carried out in collaboration with Professor Anders Rantzer at the Department of Automatic Control at Lund
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strong background in mathematics and statistics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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level in machine learning, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of
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medicine and engineering. The available position is within a research group which develops methods for analysis of medical images. The group collaborates with several medical doctors at Linköping University
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quality, greenhouse gas fluxes, and emergency response capacity. Yet, current modelling approaches are incapable of inference from diverse sensory data, too computationally demanding for real-time use, and