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, software, and High Performance Computation (HPC) infrastructure; • Excellent scientific infrastructure; • Participation in project meetings and international conferences; • Flexible working hours
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-and-egg" problem of sampling: developing algorithms that learn to bias simulations automatically without requiring prior knowledge of the mechanism. You will contribute to open-source software
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Information Modeling (BIM) and energy modeling software. Data Science Proficiency: Knowledge of data modeling, ontologies (RDF/OWL), or semantic web technologies is highly desirable. Programming: Strong
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software development, including version control, testing, and reproducible research workflows; familiarity with CI/CD practices is an advantage. Experience with supervision of BSc and MSc student projects
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. Experience working with deep learning software stacks, extensive software development experience, and knowledge of machine learning frameworks (such as transformers, torch, Megatron, triton etc.) are pluses
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project is in close collaboration with the Head of Software - Technology and Innovation at EBU. You will have an extended and paid stay in Geneva with the EBU. As part of RePIM you will participate in
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fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind
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Python and/or Matlab. Experience with software-defined communication systems, emulation tools, and machine learning techniques is considered an advantage. Most importantly, you are eager to learn and apply
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Experience with firm level econometric analyses or with processing of large firm level datasets will be an advantage, but it is more important that you have experience with software tools such as Stata, SAS, R
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familiar with quantitative statistical analysis and has at least a basic understanding of statistical analysis framework or software. If you do not have any experience with the above but are interested in