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Field
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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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assays Data science and computational protein design for library screening Special Instructions Applications should contain: A complete CV Cover letter describing research interests and goals Full list
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. • Experience with data analysis using statistical inference techniques. • Experience with health economic evaluations. • Experience with parallel and/or high-performance computing. • Familiarity with agent-based
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on competence: contributing to research software development supporting simulations and/or data workflows (HPC/parallel environments), and open/reproducible release of data and analysis scripts under FAIR
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exhaustive and is only intended to illustrate examples of possible innovative practices that could run in parallel with the faculty's strategic goals. You are free to suggest concepts beyond this list
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learning frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience inscalable data processing, including the use of parallel computing, cloud platforms,and distributed systems
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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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qualifications Experience with high-order or nonlinear scattering reconstruction methods for imaging thick and strongly scattering samples. Experience in high-performance computing, parallel programming, or GPU
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are the consequences of acquiring or not acquiring professional language skills for individuals? What measures can strengthen parallel language practices and the conditions for using Norwegian professional language