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time-series data Experience of high-performance computing, GPU-based computing, or parallel programming Experience of probabilistic modelling or inverse problems Experience of developing and
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of collaborators and strong access to compute through national GPU systems (NAISS, e.g
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rigorously. Develop, implement, and optimize signal and image processing algorithms using appropriate programming environments and computational tools. Contribute to the definition of system
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and delay-differential equations. Scientific computing and numerical analysis Large-scale dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming
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researcher, renewable up to 4 years, for interdisciplinary work applying tile-based programming abstractions to an adaptive mesh refinement computation. Salary Range: $63K – $73K – Post-doc funding
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leadership-class computing systems and institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring
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programming with one or more common parallel programming models, like MPI or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively
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strong programming skills (e.g., Python, Fortran, C++) Knowledge of data analysis techniques and statistical methods Proven scientific writing and oral communication skills Ability to work both
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and execute computational research projects Strong programming skills (e.g. Python) and documented experience building robust computational pipelines in GPU/HPC environments Solid understanding of