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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
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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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on available work, funding, and performance. Anticipated Division of Time (30%) Direct research program in AI/ML applications for climate science, including supervising team members, developing novel algorithms
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the algorithms and mechanisms that support flexible behavior. We use large-scale electrophysiology and imaging to record hundreds to thousands of neurons across multiple brain areas during behavior. To test the
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learning. At least five (5) years of experience in AI/ML/DL or equivalent combination of education and experience. Demonstrated experience in algorithm development and structured programming ability
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implementations of genomic selection in canola. Imputation (20%) – Implement genotype imputation algorithms to enable joint analysis of historical datasets genotyped using different marker panels. Presentations
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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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, developing novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting. Lead development of innovative visualization techniques and interpretable machine learning methods. (30
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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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of experience in AI/ML/DL or equivalent combination of education and experience. Demonstrated experience in algorithm development and structured programming ability. Experience in scientific programming and the