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Field
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statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity with high-performance computing or large-scale simulations
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, fined tuned for zooming in on machine spatial reasoning, is within the scope of this project. Developing efficient algorithms for converting computer simulations of a system in a complex environment (e.g
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, Presentations, and Manuscript Preparation Analysis of experimental results. The successful candidate will use different computer programs to analyze viral neutralization assays and antibody binding. Analysis
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exploiting the broadband guidance handling capabilities of modern hollow fibers filled with gases. Such light source will allow exquisite control of key light parameters such as pulse durations, emission
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PhD Research Fellow in Theoretical and Computational Active Matter Physics for Glioblastoma Invasion
programming skills Desired qualifications: Experience in statistical physics, solid mechanics, or fluid mechanics Experience with data-driven modeling, parameter estimation, or model calibration Familiarity
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Hiring Range Dependent on experience and/or qualifications Proposed Start Date 03/01/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in
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of the parameter space in relation to the statistical model. One of the main goals of SLT is to quantify the complexity of such models w.r.t. the data generating process (and some prior probability distribution
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 11 days ago
Dependent on Experience/Qualifications Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and
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that combine first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate
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quite new within the field of computer vision. The neuromorphic design allows for a much higher acquisition frequency but most and foremost much longer acquisition time spans. This makes it ideal