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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 23 hours ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form of conference
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, spectral interpretation, chemometric model development (preferably using R and/or Python). Ability to work with large spectral datasets, including developing and managing spectral libraries or databases, is
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alternatives to traditional chemical analyses, spectral data processing and multivariate analysis, spectral interpretation, chemometric model development (preferably using R and/or Python). Ability to work with
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, Python, and SAS or Stata) • Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling, experience with Medicare claims data • Clear scientific writing and
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have a Ph.D. in economics, public policy, education, or a related field by the start date, strong quantitative and econometric skills, experience coding (in Stata, Python, R, and/or SAS), and an interest
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will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form
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Postdoctoral researcher (M/F). Modeling damage during earthquakes. Comparison with geophysical data.
. Use of digital earthquake models; The code used for calculations is written in Fortran, and the tools for processing input and output data are written in MATLAB and Python. The postdoctoral researcher's
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, representation learning) Spatiotemporal modeling or geospatial/temporal data analysis Medium-to-Large-scale foundation models pretraining/fine-tuning paradigms Strong programming skills in Python and experience
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with