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in Python, Machine learning, and Uncertainty Quantification in engineering applications, Preferred: Background in space weather and astrodynamics Excellent written and oral communication skills
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structured programming language, ideally python, and should have good experience in the analysis of photometric and spectroscopic data of exoplanet atmospheres. The projects will involve the use, modification
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microplastic particles based on spectral and morphological fluorescence data. Develop and maintain modular analysis pipelines in Python or MATLAB, integrating data preprocessing, feature extraction, and
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Steven Ludeke to discuss their expected degree timeline. Highly proficient in at least one statistical programming language (e.g., R, Stata, SAS, Python). Candidates that can show an aptitude for learning
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platforms (e.g., TRNSYS-Python) Implement and test AI-enabled smart energy management strategies in real-world settings Conducting in situ measurements, including planning, setting up sensors, data collection
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CT or MRI scans. Your technical skills include Python programming and familiarity with deep learning frameworks (PyTorch or TensorFlow), along with a solid understanding of image processing techniques
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restoration in the state of SP. The proposed approach will be implemented in an online geospatial platform to support the ReflorestaSP program. Knowledge of geospatial modeling and programming (e.g. Python and
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fatigue testing of metals Skills in in situ testing, FIB sample preparation, and image/data analysis tools (e.g., DigitalMicrograph, ImageJ, Python/Matlab) are beneficial. Strong understanding
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. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages such as python. Experience with HPC environments and linear algebra
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, Urban Planning, Urban Economics, or a related field. Experience in research projects applied to urban, environmental, or socio-economic issues. Proficiency in urban modeling tools such as MATLAB, Python