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Argonne National Laboratory seeks a Postdoctoral Appointee to perform computational research on materials for thermal and electrochemical interfaces. The successful candidate will integrate first
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areas of nanoscience and nanotechnology. Job Title: Postdoctoral Researcher (Computational Materials Science - AI for Catalysis) Research area or group: Theory and Simulation Group The Australia-Spain
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Department of Chemistry and Materials Science is looking for: A Postdoctoral Researcher in computational modelling of self-organization in soft materials to join the Research Council of Finland Center of
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28 Nov 2025 Job Information Organisation/Company University of Salzburg Department Human Resources Research Field Computer science Researcher Profile Recognised Researcher (R2) Positions Postdoc
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31 Dec 2025 - 23:59 (UTC) Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
Materials Research Center (SusMat-RC) at UM6P. The successful candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models
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A postdoctoral research position is available in the area of computer modelling of new materials for next-generation photovoltaics within the group of Professor Saiful Islam in the Department
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-computational-materials-sciencemachine-learning-1 Street Andrzeja Sołtana, 7 Postal Code 05-400 E-Mail dorota.syrkiewicz@ncbj.gov.pl STATUS: EXPIRED
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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Materials Research Center (SusMat-RC) at UM6P. The successful candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models