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is carried out in close collaboration with industry and academic partners. Main responsibilities Develop a research plan and design test configurations in collaboration with senior researchers. Perform
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The Department of Mathematics and Mathematical Statistics, Umeå University, invites applications for a two-year postdoctoral fellowship in data-driven analysis of a subarctic ecosystem near Kiruna
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and design nanostructures tailored for aerospace applications. About us The Division of Engineering Materials explores the relationship between technologically important materials and their properties
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results into practical applications for end users. Subject Description The research aims to develop machine learning models for microbe detection, focusing on the mathematical foundations in geometry and
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/ This postdoctoral fellow position is part of a new Career Programme for early-stage researchers in Educational Sciences initiated by the Umeå School of Education. The Career programme aims to strengthen Educational
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the research on steel recycling and processing. Within the research subject Engineering Materials, there are several research programs spanning broad areas of metallic and ceramic materials research
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on the development of new perennial cereal grains within an extensive domestication and plant breeding program. Your research work will focus on questions of genetic and phenotypic diversity and to develop genomic
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model adequacy Experience with development of R packages Familiarity with RevBayes and probabilistic programming Ability to collaborate in interdisciplinary projects The application should consist
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. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/ Our group focuses on studying
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning