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analysis. · Advanced programming and workflow development skills in Python, R, Bash, and related computational tools for large-scale data analysis and reproducible research. · Experience with
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of Miami. Other potential undergraduate classes include Introduction to Python Programming - a requirement for the Rosenstiel BS - and Climate Change, a general education course. We are also considering
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Python and Fortran Familiarity with climate data formats (e.g. NetCDF) and relevant tools (e.g. xarray, CDO) Proficiency with Linux/Bash and Git version control Experience with HPC/supercomputing
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Time-Domain Spectroscopy, Hall measurements, Raman microscopy, device fabrication, Scanning Electron Microscopy, Instrument control in LabVIEW or python, synthesis of 2D materials, 2D materials
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methods, strong publication potential, good command of English, programming skills in MATLAB, Python, R, Julia, or similar. Experience in one or more of the following is an advantage: time series analysis
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or data analysis Training & experience in econometrics and causal inference methods, particularly in the context of randomized controlled trials (RCTs) Proficiency in R, Python, Stata, or similar tools
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mechanics) with data-driven techniques. • Proficiency in Python (NumPy, TensorFlow/PyTorch) and MATLAB. • Familiarity with deep learning architectures, particularly recurrent neural networks (e.g., LSTM) and
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with Bayesian inference or machine learning is an advantage. • Proficiency in Python and/or R; experience with probabilistic programming (Stan, PyMC, NumPyro) or deep learning frameworks (PyTorch, JAX
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of experience in logistics and supply chain management is an advantage. Those without relevant work experience but who have the technical skills are welcome to apply. Proficiency in Python or similar programming
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datasets, registries, or electronic medical records • Strong knowledge of causal inference and comparative effectiveness methods • Experience using statistical software such as R, Stata, SAS, or Python