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Qualifications Experience with hyperspectral data acquisition and analysis, including Python-based workflows. Familiarity with machine learning, data integration, and advanced visualization tools for exploration
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cross encoder architectures and retrieval augmented generation. Strong programming skills in Python and deep learning frameworks, familiarity with MLOps practices, data governance for sensitive health
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desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong communication and collaboration skills; ability to work independently and as part of a team. Willingness to respect
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in conducting in-depth analyses using different teaching-related datasets through programming languages (e.g., R, Python, Matlab, etc.) to derive patterns and trends for evaluation and educational
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ability to work with internal and external stakeholders. High level of proficiency with MS Office Suite (Word, Excel, Access, Outlook), Windows. Knowledge of Linux/ Python would be an asset. As one
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. Applicants must be familiar with the course content (please consult the eCalendar) Some experience with Python coding Knowledge of statistics and/or data science is an advantage roficiency in English is
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. Analytical Instrumentation: Expertise in material characterization (XRD, SEM, EDX, UV-Vis spectroscopy) and electronic testing (Source Measure Units). Data Analysis & Design: Skills in Python, MATLAB
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testing (Source Measure Units). Data Analysis & Design: Skills in Python, MATLAB, or LabVIEW for data acquisition, alongside familiarity with optical simulation or CAD software. Required Qualifications
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Python, MATLAB, or LabVIEW for data acquisition, alongside familiarity with optical simulation or CAD software. Required Qualifications Educational Requirements: A Ph.D. in Chemical Engineering, Materials
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modeling, data analysis, or remote sensing. Experience with numerical modeling tools (e.g., WRF, CESM) and programming languages such as Python, MATLAB, or Fortran. Familiarity with observational techniques