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Field
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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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systems for extreme-scale, heterogeneous computing. This role sits at the intersection of compiler infrastructure, formal verification, runtime systems, and hardware/software co‑design—expanding into
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algorithms for analysis of large image datasets. Experience with parallel computing, high-performance computing, distributed computing, and/or cloud-based computational workflows. Experience with large-scale
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Center (HT- RDS Center). The applicant would be responsible for designing and developing workflows for automated, micro-scale parallel experimentation. These workflows will be used for the
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approaches to study RNA turnover or with massively parallel reporter assays (MPRAs) will be beneficial. Must Have Bioinformatics experience in the analysis of (RNA) sequencing data Experience with common lab
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with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows. Demonstrated technical
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focusing on: Next-generation energy storage systems Power converter design for Power-to-X (PtX) solutions Microgrid applications Quantum computing in power systems And other emerging technologies in
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, and development of tools suitable for high-performance computing environments. Experience with parallel computing and workload management systems such as SLURM is highly desirable. Model Calibration
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Job Description Do you want to contribute to the green energy transition? Are you excited about developing intelligent power electronic converters and drives with AI and edge computing technologies
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will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly generated data. The world will see all too many changes