231 parallel-and-distributed-computing-phd positions at MOHAMMED VI POLYTECHNIC UNIVERSITY
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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 staff
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
To be considered for this role, you will ideally have: PhD in Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular
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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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related to computational chemistry for the benefit of Master and PhD students at UM6P, Assist in the delivery of engineering solutions to enable the rapid evolution of applied research through the various
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position in computational research in interface chemical reactions to join our research team focused on studying reaction mechanisms and materials. The ideal candidate will have a strong background in
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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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To be considered for this role, you will ideally have: PhD in Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular
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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