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using simulation-based parametric studies. Couple computational results with experimental data for model calibration and validation. Apply sensitivity analysis and optimization algorithms to refine model
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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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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
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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel algorithms and methodologies for tensor completion in multidimensional
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3 Dec 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Engineering Researcher Profile Recognised Researcher (R2) Established Researcher
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Researcher (R3) Country Morocco Application Deadline 17 Dec 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
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3 Dec 2025 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country
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into optimization frameworks. Study the theoretical foundations of randomized algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities