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to an entrepreneurial ecosystem directly related to its industrial and socio-economic environment in Morocco and Africa. HTMR provides highly automated and parallel approaches to the development of new materials and
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, model predictive control, parallel computing using JAX and rapid online learning, is highly desirable, but candidates demonstrating an ability and willingness to become familiar with these topics and able
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, model predictive control, parallel computing using JAX and rapid online learning, is highly desirable, but candidates demonstrating an ability and willingness to become familiar with these topics and able
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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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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 5 days ago
well as software developments for many parts of the system up to high-level science analysis tools. The construction of the first CTAO telescopes in Chile will begin in 2026, in parallel to the first Pathfinder for
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the workpackage team and collaborating with partners across Europe: data sharing, code exchange, joint publications. Your qualities You hold a PhD (or near completion) in statistics, applied mathematics, data
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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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of biology and the effect of mutations. He/she would also be required to have a sound computational background, especially in the area of simulations. Criteria of the candidate: PhD in the field
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fertilizer production, is a major starter client for the University providing capital and research funds. In parallel, a new portfolio of clients is growing with the development of an R&D cluster around the
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use