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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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Technician - Dept of Neurobiology & Behavior (CAS) - Technician II The Fernandez-Ruiz & Oliva labs employ a multi-disciplinary approach, including the development and application of cutting-edge experimental
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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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The Fernandez-Ruiz & Oliva labs employ a multi-disciplinary approach, including the development and application of cutting-edge experimental and computational techniques, to understand
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quantitative geneticist with demonstrated expertise in the development and testing of genomic prediction models for plant breeding applications. The individual will work closely with the canola breeding team
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requires an experienced quantitative geneticist with demonstrated expertise in the development and testing of genomic prediction models for plant breeding applications. The individual will work closely with
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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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using the MTM dataset, and administers a mini-grants program to engage a wider community of researchers and EdTech developers. The work includes, but is not limited to: (1) working alongside the NTO's
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%) Direct research program in AI/ML applications for climate science, including supervising team members, developing novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting
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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