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Field
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, statistics, machine learning, programming in C, C++, Python Scripting languages and MATLAB, using a parallel computing environment Bioinformatics, network biology, network medicine, network analytics, AI, ML
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; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging massively parallel algorithms and code performance profiling are a plus. Special
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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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mechanics. Familiarity with earthquake physics, induced seismicity, geothermal energy, carbon storage, or subsurface engineering applications. Experience with parallel computing, scalable workflows
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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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mechanics. Familiarity with earthquake physics, induced seismicity, geothermal energy, carbon storage, or subsurface engineering applications. Experience with parallel computing, scalable workflows
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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