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). Familiarity with wilderness policy and the National Wilderness Preservation System. Proficiency in R, Python, or GIS platforms. The Successful Candidate Will Possess excellent written and oral
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, python and/or MATLAB, scientific computing and numerical methods. Experience with computing, PyTorch, spectral CT, photon-counting detectors, or preclinical imaging would be helpful but is not required
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-omic data integration. Computational skills for data analysis (e.g., Python/R, workflow tools, and reproducible research practices). Prior work related to neurodevelopmental disorders, regulatory
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optimized for single-molecule mechanical manipulation. Develop, refine, and implement automated data analysis pipelines using Igor Pro (or MATLAB, Python). Mentor graduate and undergraduate students within
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hold a Ph.D. in materials physics or scientific imaging, with proven experience in spectral imaging techniques (e.g., software such as Python, R, HyperSpy) and/or synchrotron techniques (luminescence, UV
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for microscopy, spectroscopy, interferometry, or quantum-optics experiments. Experience acquiring and analyzing experimental data using Python, MATLAB, LabVIEW, or comparable scientific software. Preferred
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/instruction tuning methods. * Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization. * Strong programming capabilities in Python
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experience with piezoelectric- or ferroelectric nitrides is an advantage Knowledge of PVD process development and pulsed sputtering is an advantage Proficiency in Python programming Interest in lab-automation
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(PIT-tags, hydrophones, geophones, etc.). 2) Modeling and data analysis: - Proficiency in GIS tools (QGIS, ArcGIS) and hydraulic modeling software (HEC-RAS, TELEMAC, Mike). - Advanced programming (Python
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, such as R, Python, and statistics, and to learn collaboration and leadership skills through workshop and group experience. USDA-ARS Mentor: If you have questions about the nature of the research, please