16 statistics-"https:"-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" positions at Linköping University
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graduated at Master’s level in machine learning, statistics, computer science, fluid mechanics, or a related area that is considered relevant for the research topic of the project, or have completed courses
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine
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biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in
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, preferably second cycle level courses, and that the applicant has proficiency in statistical process control. Teaching is mostly done in English, thereby requiring adequate English language proficiency. It is
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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, 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 240 credits, at least 60 of which must
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) - experience of some type of remote sensing - knowledge of agriculture - knowledge of biodiversity, biological communities, and different habitats - experience in calculations, statistical methods, and modelling
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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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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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cybersecurity tool development, virtualised networking test beds, application of statistical or AI methods for load monitoring and performance tuning, and anomaly detection are prioritized. Excellent