60 high-performance-computing-postdoc Postdoctoral positions at Texas A&M University System
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processing, characterization, and mechanical testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer
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stakeholders through outreach activities. This and another postdoc fellow will be stationed at the USDA ARS OCPARC lab in El Reno, Oklahoma and will collaborate with ARS and TAMU scientists
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stocks of manure and agricultural biomass in the region. The postdoc will work closely with CPRL scientists and collaborators to conduct lab‑, pilot‑, and feedlot‑scale research to develop and evaluate
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to support U.S.–Mexico treaty-based water allocation and infrastructure planning. The postdoc will have opportunities to work at the interface of research and practice, interacting closely with U.S
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technical progress reports to research sponsors. -Ensures program has sufficient and efficient computing capabilities (high performance research computing, workstations, software, network attached
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with advanced characterization tools (e.g., PPMS, XRD, SQUID magnetometry, optical spectroscopy, or scanning probe microscopy). Experience with high-performance computing or cloud-based machine
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stakeholders through outreach activities. This and another postdoc fellow will be stationed at the USDA ARS OCPARC lab in El Reno, Oklahoma and will collaborate with ARS and TAMU scientists. Responsibilities
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working with high-performance computing environment. Experience in data management and developing data-processing pipelines. Proficiency in programming languages such as R, Python, Matlab. Familiarity
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approaches for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data
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coordination with the PI and project objectives. Process, clean, curate, align, and analyze high-resolution image and process datasets; develop reproducible pipelines for data quality assessment