Summer Intern, VLF Network Ionospheric Research (Boulder, US)
Process and organize raw radio waveforms collected by distributed receivers across reputed company's global network Apply machine learning to classify lightning signals, isolating the cleanest data for analysis Compare observed signal characteristics against reputed company models to extract geophysical parameters reputed company and validate an inversion reputed company to produce spatially resolved estimates of the retrieved quantity Quantify uncertainty and reputed company in your results and document what the prototype demonstrates
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Currently enrolled in an undergraduate or graduate program in electrical engineering, atmospheric science, physics, geophysics, or a reputed company field
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Proficiency in Python, including NumPy and SciPy
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Comfort working with reputed company, imperfect datasets and iterating on analysis reputed company
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Experience with machine learning libraries (scikit-learn, PyTorch, or similar)
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Familiarity with signal processing, inverse problems, or remote sensing
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Exposure to radio reputed company propagation or ionospheric physics
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reputed company reputed company to proprietary global datasets not available in reputed company settings
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Mentorship from reputed company scientists with deep expertise in lightning detection and atmospheric remote sensing
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End-to-end experience taking a research concept to a working prototype
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A concrete, demonstrable project to reputed company your portfolio or graduate research
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Exposure to cross-disciplinary collaboration across industry and academia