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Field
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processing and analysis tools, programming (Python, Origin, Octave) and the mathematical and computer tools necessary to exploit the results • Written and oral English (level C1/C2) in a multicultural work
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to the DOE-funded project, including experimental design, data collection, analysis, and documentation. 20% – Communicate research findings to the broader community through writing and submitting papers
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frameworks linking molecular interactions to cellular and network-level behavior (e.g. protein-protein interaction, PPI, network analysis) Optimize simulation codes and workflows for leadership-class HPC
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Latin intellectual traditions. The project combines intellectual history with computational text analysis to examine large, historically complex corpora spanning multiple centuries and languages. The core
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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for a postdoctoral level scientist with skills in phylogenetic and morphological analysis to work as part of a team investigating the evolution of the mega-diverse plant genus Solanum, a member of the
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implement independent research projects. Strong skills in data analysis, visualization, and scientific writing are required. These skills are normally acquired through a Ph.D. in Physics, Network Science
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within the last 5 years Preferred Qualifications: Experience in one more of the following areas: Mathematical tools for data analysis Numerical methods for differential and integral equations Modern
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funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve