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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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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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to improved system design. The research program may focus on small scale technologies to improve production (e.g., sensors) or large-scale optimization (e.g., regional economic models) or anything in between
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that the annotation models implemented match user needs and expectations Translate findings into requirements for the engineering team to inform the algorithms, models, annotations, and ultimately the data is made
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to improved system design. The research program may focus on small scale technologies to improve production (e.g., sensors) or large-scale optimization (e.g., regional economic models) or anything in between
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learning algorithms and other innovations Partner with the accelerator physics group on research endeavors, such as wakefield calculations and electron cloud studies Control Software Group Leadership
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in requirements gathering to ensure that the annotation models implemented match user needs and expectations Translate findings into requirements for the engineering team to inform the algorithms