17 parallel-and-distributed-computing uni jobs at Chalmers University of Technology in Sweden
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Shape the future of high-performance computing. The European DARE project is pioneering next-generation processing technologies, leveraging RISC-V and Vector architectures, and we're looking for a
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Application Deadline 6 Sep 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Part-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 304--1
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specialise in nanoparticles formulated from lipids. We characterise the composition and distribution of lipid molecules in both synthetic and naturally occurring nanoparticles (including extracellular vesicles
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motivated PhD candidates who want to enter a doctoral program at the forefront of science. Our PhD students develop abilities to plan, perform, critically review, and present their research. PhD studies
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2025 - 12:00 (UTC) 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 within
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6 Sep 2025 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Other Engineering » Materials engineering Physics » Other Researcher Profile First
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Application Deadline 19 Sep 2025 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 304--1
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into molecular structure-property relationships, and obtain understanding of chemical reactions. We frequently collaborate with colleagues from synthetic organic chemistry, computational surface science and
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Doctoral student in Materials Chemistry of Doped Organic Semiconductors in EU Training Network FADOS
Researcher (R1) Country Sweden Application Deadline 6 Oct 2025 - 22:00 (UTC) 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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actions to evaluate, balancing safety and computational effort. You will compare deep learning–based methods and probabilistic machine learning approaches, and explore extensions to active reachability