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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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the postdoctoral appointment’s nature as a career-development position for junior researchers, we are looking for candidates who have completed their PhD no more than three years before the application deadline
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industry and wider society Administration related to the work duties listed above Qualification requirements Appointment to a post-doctoral position requires that the applicant has a PhD, or an international
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ecology. Your profile The successful candidate shall have a PhD degree in environmental sciences or a related field with a good understanding of soil and aquatic ecosystems, biogeochemical cycling
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computational and statistical methods, with demonstrated experience in reproducible and scalable bioinformatics environments. The applicant will work in close collaboration with other Engblom lab team members who
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Experience with satellite remote sensing and geographic data Excellent English communication skills (spoken and written) Highly desirable skills: Experience in GIS, remote sensing, statistical analysis and
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computational and statistical methods, with demonstrated experience in reproducible and scalable bioinformatics environments. The applicant will work in close collaboration with other Engblom lab team members who
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models into Chalmers’ bridge simulators in collaboration with other researchers. You are also expected to supervise PhD and MSc students and to publish at least two peer-reviewed journal articles during
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, their development, and how disruptions in homeostasis contribute to pathological conditions. The position involves close collaboration with PhD students, postdoctoral researchers, and international partners. Active
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. Work tasks Statistical analyses of biological data, primarily for herring and cyprinids, collected from the commercial fishery as well as from scientific surveys in the Baltic Sea to assess spatial