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dynamos and connect them to exoplanet discovery and space‑weather environments. You will work in Aalto’s High‑Performance Computing Lab within the PEAS (Population of Exoplanets around Active Suns
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Experience with multimodal and/or multitemporal data. Experience with Earth observation or regression datasets is an advantage Experience working on High-Performance Computing (HPC) systems, including batch
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available datasets to interpret results in a robust biological context Ensure reproducibility, data management, and documentation of computational workflows in high-performance computing environments
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‑weather environments. You will work in Aalto’s High‑Performance Computing Lab within the PEAS (Population of Exoplanets around Active Suns) consortium alongside leading teams in stellar astrophysics
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publicly available datasets to interpret results in a robust biological context Ensure reproducibility, data management, and documentation of computational workflows in high-performance computing
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do we build and operate a high-performance computing infrastructure for cutting-edge research that simultaneously meets the highest environmental and economic standards? Using a prospective life
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particular, we value academic excellence and/or a proven record of scientific achievement, combined with the ability to develop original ideas, build high-performance experimental setups, demonstrate
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computational tools. Maintain reproducible workflows and manage data on local and high-performance computing resources. Essential Function Yes Percentage of Time 35 Job Duty Develop and apply machine
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Eindhoven University of Technology (TU/e) | Eindhoven, Provincie Noord-Brabant | Netherlands | 2 months ago
high demand. You will be supervised by Dr Gabriele Liga ( https://www.tue.nl/en/research/researchers/gabriele-liga ) and Dr Yunus Can Gültekin at TU/e. You will carry out two research secondments: one
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-performance computing infrastructure, and evaluation using objective metrics and subjective listening tests. Your network and team You will be supervised by Assistant Professor Lauri Juvela , who leads the