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technological development. As a PhD student in this project, you will operate and evaluate different laboratory-scale biogas processes with the aim of optimising both biogas production and biofertiliser quality
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:00 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position
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the influence of climate-driven changes in vegetation cover and winter conditions on habitats and food webs of Arctic/alpine lakes. Climate change is promoting the expansion of terrestrial vegetation, which
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PhD Position in Engineering Materials Project: Development and Characterization of Advanced Sorbents
, with a focus on the design and development of advanced sorbent materials for environmental and gas-cleaning applications. The research subject “Engineering Materials” has ongoing research programs span
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Researcher (R1) Application Deadline 30 Oct 2026 - 22:59 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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personal development. For more information about our PhD program, please visit our information page. We offer fully financed positions to our doctoral students who receive competitive salaries for their work
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thriving research environment. You will also have many opportunities to enhance your teaching skills and personal development. For more information about our PhD program, please visit our information page
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-authored. Present the project and its research findings at academic conferences. Develop ways of disseminating the project's results to wider audiences, for example through blog posts, newspaper articles
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ecosystems. SciLifeLab and Wallenberg's national program for data-driven life science (DDLS) aim to recruit and train the next generation of researchers in data-driven life science and create globally leading
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the doctoral education programme. The work includes development, implementation, and evaluation of statistical and machine-learning-based methods for calibration and fast simulation of nuclear fuel performance