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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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approaches, including machine-learning methods, that improve the identification or enumeration of overlapping vocal individuals. Array localization, spatially explicit capture-recapture, distance sampling
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studies will also be considered. Computational methods, content analysis, surveys, and other methods can be used complementarily, but should not be the primary method. Viable path towards access
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analytical or quantitative techniques (e.g., GC–MS, chromatography, or related analytical methods) Background in early-season pest management, seed pathology, or soilborne pathogen systems. Additional
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experimentation • Experience working in applied agricultural systems or IPM-focused research • Experience with analytical or quantitative techniques (e.g., GC–MS, chromatography, or related analytical methods
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designs and regional-to-continental scale investment of monitoring resources. Developing methods to quantify and propagate uncertainty across integrated monitoring frameworks Developing methods for joint
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are especially excited to hear from candidates eager to invent new tools, wetlab approaches for circulating nucleic acids, interpretable machine learning for biomarker discovery, and methods we haven’t imagined
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studies will also be considered. Computational methods, content analysis, surveys, and other methods can be used complementarily, but should not be the primary method. Viable path towards access
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, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or related computational field. Demonstrated experience in epidemiological methods, preferably using national mortality datasets
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related computational field. • Demonstrated experience in epidemiological methods, preferably using national mortality datasets and/or case-crossover analyses, and have a promising publication record