34 parallel-computing-numerical-methods-"Simons-Foundation" positions at Linköping University in Sweden
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treatment. It is advantageous if you have previously worked with massively parallel sequencing (NGS), both in the laboratory and/or with bioinformatics, for example Single Cell RNAseq. It is also advantageous
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. For this you will develop and refine techniques taking advantage of apps, intelligent agents, AI assistants, or similar. You will be encouraged to publish scientific findings and share new analysis methods, as
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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Your tasks will include conducting independent research in the subject area at
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various organoid and cell culture methods, primary cell isolation, imaging and sequencing techniques) with capability in using computational tools (competent with R, python and command line tools) and are
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characteristics. Innovate methods for integrating photosynthetic components into material systems and devices with additive manufacturing. Functionalize biological components with technological and stimuli
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Application Deadline 31 Oct 2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not
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First Stage Researcher (R1) Country Sweden Application Deadline 29 Sep 2025 - 22:00 (UTC) Type of Contract To be defined Job Status Full-time Is the job funded through the EU Research Framework Programme
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- 12:00 (UTC) Type of Contract Permanent 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 a
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities