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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
Application Deadline 12 Jan 2026 - 00:00 (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
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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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transfer, turbulence modeling, and reaction kinetics. Experience with high-performance computing (MPI, parallel simulations) is highly desirable. Demonstrated ability to work independently and
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framework (e.g., OpenFOAM, ANSYS Fluent). Solid understanding of heat and mass transfer, turbulence modeling, and reaction kinetics. Experience with high-performance computing (MPI, parallel simulations) is
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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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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Application Deadline 26 Nov 2025 - 00:00 (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
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opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel
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ecosystems accumulate carbon and restore nitrogen and phosphorus availability via re-distribution within different forms of nutrients present in soils, reduced losses, and inputs from biological N fixation
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. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages such as python. Experience with HPC environments and linear algebra