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, (Bio)informatics, Computer Science, Mathematics or a related quantitative field. Candidates with PhD in biomedical science with genomics data analysis training and experience are also encouraged to apply
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sheath). Candidates with strengths in theoretical modeling, asymptotic analysis, or numerical simulations are also encouraged to apply. Required Minimum Qualifications: PhD in Mechanical Engineering
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Pennsylvania State University, Department of Mathematics Position ID: PennState-POSTDOC3 [#28088, REQ_75427] Position Title: Position Type: Postdoctoral Position Location: University Park
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Pennsylvania State University, Department of Mathematics Position ID: PennState-POSTDOC2 [#27138, REQ72094] Position Title: Position Type: Postdoctoral Position Location: University Park
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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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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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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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exploration and scale-up analysis. You should possess a PhD in (bio-)chemical engineering, bioengineering or another related field, with extensive experience in the mathematical modelling of biological
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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