147 parallel-computing-numerical-methods-"DTU" Postdoctoral research jobs at MOHAMMED VI POLYTECHNIC UNIVERSITY
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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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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
based on experimental and computational data. Establish predictive models based on artificial intelligence methods. Utilize the developed models to design and optimize the properties of materials. Conduct
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to improve reactor design and synthesis performance. Key Responsibilities: Develop comprehensive computational models for fluid dynamics, mixing, and reaction kinetics in the CSTR. Implement advanced numerical
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. The objective of the research is to use an integrated approach combining numerical and analytical techniques, simulations and analysis of available experimental data to study and provide efficient
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) Established Researcher (R3) Application Deadline 9 May 2026 - 00:00 (UTC) Country Morocco Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by
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Job Description As part of our laboratory's research initiatives, we are conducting advanced research on the computational modeling and optimization of heterogeneous catalysts for various catalytic
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UM6P College of Computing: The College of Computing, with a campus in Rabat and a campus in Benguerir, is a central component of the University Mohammed VI Polytechnic (UM6P). The main goal
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the project’s next phase. Advanced technical expertise is not required at the outset; rather, the fellow should demonstrate a willingness to learn new tools and to think critically about how computational methods
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10 Apr 2026 Job Information Organisation/Company MOHAMMED VI POLYTECHNIC UNIVERSITY Research Field Computer science Economics Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning