47 linked-data-"https:"-"https:"-"https:"-"University-of-Southampton" Postdoctoral positions in Sweden
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collaborates with several national and international research groups on these topics, and has active links with the private and public sectors. For more information see: https://www.umu.se/en/research/groups
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dynamic and supportive research environment with access to world-class infrastructure, strong clinical links, and excellent opportunities for scientific development Duties The successful candidate will lead
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how data, carbon credits and financial resources circulate among actors linked to carbon farming programs. Selecting case study sites in close collaboration with the project team, guided by insights
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comprehensive support for large‑scale multi‑omic data generation/analysis and transformation/embryogenesis services for functional validation. Nathaniel is also an associate group leader at the Science for Life
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ultrafast optics developments. More information is available at www.attolund.se . Subject description The positions are linked to the attosecond group at the department. The group's research includes
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in the research group for AI and society led by Stefan Larsson at LTH in Lund. The group focuses on social science-oriented but multidisciplinary issues linked to AI. The group collaborates e.g. over
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courses and grades, Copy or download links of doctoral thesis and up to three relevant articles, At least one signed letter of recommendation from referees who are qualified to assess the applicant’s
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Description of the workplace The postdoctoral position is linked to the Division of Clinical Chemistry at the Faculty of Medicine, Lund University. However, all work will be in close collaboration
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Intelligence (AI). For more info, please visit link . LUCI is well-established, with an extensive group of affiliated PhD-students, postdocs and more senior researchers from diverse disciplines, with a close
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches