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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
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smoothly by managing reagents and supplies and performing genomic assays and assisting with long read Nanopore sequencing, functional genomics, RNA IP, RNA probe synthesis and Massively Parallel reporter
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parallel programming. ● Experience with writing scientific articles. ● Experience with writing scientific machine learning. Overtime Status Exempt: Not eligible for overtime Appointment Type Restricted
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are meaningful in scientific contexts.Preferred:Background in biomedical data, healthcare, or AI for life sciences.Experience with parallel computing.Familiarity with scientific machine learning approaches (e.g
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parallel/multiplexed assays, etc.) is desirable. Ability to interpret and discuss experiments and critically contribute to writing of manuscripts and grant proposals is expected. Well-organized, able
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of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning