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, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits , take well-deserved breaks with ample paid
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE2 [#27206] Position Title: Position Location: Oak Ridge, Tennessee 37831
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE [#27204] Position Title: Position Type: Postdoctoral Position Location: Oak Ridge
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) Country Finland Application Deadline 30 Nov 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
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Application Deadline 20 Oct 2025 - 01:30 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 29 Sep 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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retirement savings program with employer contributions. Unlock your potential with tuition benefits , take well-deserved breaks with ample paid time off and observed holidays , and rest easy with life and
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National Laboratory is committed to employee success and we believe that a comprehensive employee benefits program is an important and meaningful part of the compensation employees receive. Review more
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position is part of a collaborative research initiative between the Materials and Systems for Nanotechnologies (MSN) and Chemical C Computational (CC) teams at UM6P. The work aims to develop robust
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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