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that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute
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(e.g., compressors, reaction kettles). Research advanced process control strategies and deploy them on the center's distributed control systems and edge computing devices. Work closely with industry
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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) Exchange and close collaboration with partners from physics, computer science, and social psychology Your Profile: Completed master`s degree followed by a doctorate in physics, computer science, mathematics
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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of Cyanobacteria and carrying out experiments with those bacteria, as well as flow cytometric analysis. Key Responsibilities: Develop and optimize extraction methods for recovering ultra-long, high molecular weight
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characterisation Conduct advanced postprocessing of battery EIS data (e.g., using distribution of relaxation times) Collaboratewiththerestoftheteamacrossprojectsonmodelling,optimization,and electrochemistry
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supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and
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Stage Researcher (R1) Country Sweden Application Deadline 5 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded
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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models