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Field
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Research Infrastructure? No Offer Description This PhD project explores how prebiotic organic matter may have interacted with minerals on the early Earth to form some of the earliest sedimentary structures
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to national environmental and climate goals as well as to the Sustainable Development Goals. We are situated at Campus Ultuna in Uppsala and in Skara. Read more about the department here (https://www.slu.se/en
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well as in urban areas. See also: https://www.slu.se/en/about-slu/organisation/departments/ecology/ About the position The researcher will work in the field (in southern Sweden), with statistical analyses, and
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, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department-of-computing-science/ . Project
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more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Development of statistical methods for estimating plant population size and change Mathematical
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to detail, scientific rigor, and a structured, systematic approach. High motivation and strong interest in developing scientific expertise and achieve research goals. A collaborative and communicative team
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) , was created. The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ) which is a centre of excellence for experimental plant research and forest
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are linked to vascular co-option, abnormal angiogenesis, and a strongly immunosuppressive tumour microenvironment, enabling tumour cells to spread along vascular structures. This often results in residual
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/tensions between the global North and global South. We will also consider applicants focused primarily on Swedish/Nordic cases or topics. For full information of the five REMESO research streams see: https
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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