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of the studied events Exchanging results with the project team and collaborators Publication of results in international journals and at conferences Your qualifications: A Master’s degree (or equivalent) and a PhD
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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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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detection by combining measurements of time- and position-dependent changes in electrical impedance with real-time high-speed distance measurements. In a parallel project, another Postdoc will, partly based
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-dependent changes in electrical impedance with real-time high-speed distance measurements. In a parallel project, another Postdoc will, partly based on two current ongoing PhD projects, design and test
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month 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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in heterogeneous integration. Other related functions as assigned. Required Qualifications A PhD received within the last 3 years in Electrical and/or Computer Engineering or a closely related
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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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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