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agencies. -Opportunity to participate in mentoring of students. -Other duties as required. Required Education: -PhD in Entomology or related field Preferred Experience: - Experience with chemical/molecular
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors
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areas, such as AI/ML algorithms and theory. We also encourage applicants with a strong computer science background and research in applied AI/ML to apply. This is a 9-month tenure-track position located
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credit hours per semester (two courses), reflecting our strong emphasis on research. Faculty benefit from access to robust research resources—including WRDS, Bloomberg, CRSP, and DataStream—as well as PhD
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credit hours per semester (two courses), reflecting our strong emphasis on research. Faculty benefit from access to robust research resources—including WRDS, Bloomberg, CRSP, and DataStream—as well as PhD
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will be published in peer-reviewed journals and technical reports, and algorithms and models will be shared with stakeholders and the scientific and forest management communities. Engage with industry
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algorithms. Train, test, and optimize AI/ML models for production environments. Integrate AI/ML components into web-based and backend systems. Ensure security, scalability, and maintainability of applications
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emissions and soil health parameters. Results will be published in peer-reviewed journals and technical reports, and algorithms and models will be shared with stakeholders and the scientific and forest
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professional journals. Attend meetings with project sponsors and collaborators. 5%: Other duties as assigned. Education and Experience: - PhD Education, Information Technology, Engineering or related field
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algorithms and accompanying documentation to augment autonomous vehicle behaviors. - Produces efficient code subject to computational resources to function in real-time systems. - Tests and deploys programs