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. Familiarity with parallel programming is highly desirable. Proficient knowledge of English is highly desirable. A background in power systems is desirable, though not required. Your application: To inquire
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, computer science, mathematics, statistics, economics, or related disciplines, an interest for the application of operations research in energy systems and strong programming skills. Familiarity with parallel
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Max Planck Institute for Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
Model. In parallel, the group is engaged in hardware activities related to the development and implementation of new FPGA-based trigger processors, both for the operation of the ATLAS muon spectrometer
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algebra of assembly (sequential, parallel, feedback, refinement); semantic projections and system-level invariants over trajectories. Architecture synthesis as an inverse problem — given a specification
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operations with a security mindset. You will actively shape the security architecture of a live, production HPC / ML environment, keep our systems hardened, and build the processes and infrastructure
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | 3 months ago
of biomolecules. All models will be experimentally validated using data generated in parallel projects, either relying on droplet microfluidics (for thermodynamics and kinetics) or on large-scale setups (process
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modernization of official statistics production. The goal is to explore new methods of data processing, data usage, and analytical techniques, evaluate their potential in statistical production, and integrate
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-management skills: you structure complex, parallel processes, align many stakeholders and drive topics to closure, combining strategic thinking with meticulous execution Strong analytical and data skills
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the application for a UniGR fellowship. Staff members of Saarland University are not eligible to apply. The selection process for the UniGR fellowship runs in parallel with the other fellowships and is carried out
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exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and