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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Horizon Europe - EIC Reference Number 101161312 Is the Job related to staff position within a Research Infrastructure? No
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Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate predictions Ability to work in a team Ability to communicate orally in
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collaboration. The PDRA will gain experience in managing a complex bilateral programme, authoring outputs with direct policy impact, and strengthening partnerships that will continue well beyond the funding
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revolutionize the understanding and advancement of human health by conducting interdisciplinary foundational research, developing and harnessing advanced computational approaches, and training the next generation
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disease activity. This is an excellent opportunity to contribute to a collaborative research program dedicated to improving our understanding of ALS and informing future clinical trials. You'll also benefit
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United Kingdom Application Deadline 14 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We have developed alternative synthetic pathways
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Subject description The research will focus on the development
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techniques to enhance MT use, thus improving the translator's working environment and job sustainability, as well as the user experience. To better understand this complex process, INCREC triangulates data