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Field
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cold solid tumors, remain resistant to current immunotherapies. A major goal in the field is therefore to develop new strategies that increase tumor immunogenicity and improve anti-tumor immune responses
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for you! About us Our team is part of the Division of Geoscience and Remote Sensing . The division's overall aim is to develop advanced methods and instruments to observe and understand the Earth system
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groundwater CH4 production and supply across land-water boundaries are poorly understood, as is the sensitivity of this supply to ongoing climate change. This project will explore the biogeochemical and
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are looking for a highly motivated postdoctoral researcher with strong analytical and programming skills, solid expertise in computational biology or bioinformatics, and a genuine interest in translational
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epidemiology and biology of infection with start date 1 September 2026, or as agreed. The future of life science is data-driven. Will you be part of that change? Then join us in this unique program! Data driven
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of injustice when green investments target rural land. In this project we suggest that a more reciprocal approach to energy futures in rural areas is needed in which countryside contributions are highlighted and
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technology Researcher Profile First Stage Researcher (R1) Application Deadline 14 May 2026 - 21:59 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research
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tasks Northern high latitudes are subject to pronounced warming, with expected major impacts on the carbon cycle of coupled land and water ecosystems. This project aims to investigate climate impacts
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research initiatives, such as WASP and ELLIIT . You will have access to state-of-the-art computing infrastructure for machine learning, e.g. through Berzelius . Linköping University will also host the new
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missions are to push the boundaries of solid-state physics by developing novel low-dimensional materials, exploring superconducting spintronics, and advancing quantum technologies through modelling quantum