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: Appropriate research areas may include but are not limited to: Development and utilization of rapid, inexpensive, and/or in situ sensors for real-time detection of chemical hazards e.g., allergens, heavy metals
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during food processing and storage. Chemical Food Safety : Appropriate research areas may include but are not limited to: Development and utilization of rapid, inexpensive, and/or in situ sensors for real
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of Mathematics at Cornell. Members of the lab engage in interdisciplinary research, drawing on approaches from geometry, topology, graphs and networks, probability/statistics, and algorithm design, in conversation
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for controlled environments with economic impact in New York State and beyond. The successful candidate will use novel methodologies, digital tools, and sensors to understand and optimize CEA crop physiology and
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., operate sensors, instruments, and equipment for data collection, analysis, and management. Excellent communication skills (both oral and written). Strong organizational skills. Ability to prioritize
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Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms for smart energy systems to enable smart and healthy
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novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting. Lead development of innovative visualization techniques and interpretable machine learning methods (30%). Drive
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science, including supervising team members, developing novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting. Lead development of innovative visualization techniques