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learning and artificial intelligence (AI) approaches for crop management, yield prediction, and decision-support systems. Additional responsibilities include preparing manuscripts for publication in peer
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compliance (5%). •Conduct research on artificial intelligence (AI) and machine learning (ML) to analyze large-scale biological, genomic, metabolomic, and phenotypic dataset (25%). •Investigate the genetic
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agronomic intelligence products that guide precision management at the sub-field level and inform conservation decisions. The role includes integrating multi-source datasets, analyzing yield stability over
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to advance research and outreach objectives. Proficiency in the use of artificial intelligence (AI) and emerging digital tools to support research output while maintaining accuracy, ethics, and research
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acoustic technologies, artificial intelligence, and ecological field research to address complex conservation challenges. The successful candidate will bridge wildlife science and audio engineering to design
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for greenhouses and vertical farming systems. The successful candidate will focus on robot hardware development, automation system design, machine vision, and intelligent control, supported by physics-based
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Microsoft Office (Excel, Word, Outlook). • Availability to work evenings and weekends as needed. Preferred Knowledge, Skills, and Abilities: • Experience with LC-MS, GC-MS, Raman spectroscopy, and NMR
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biotic tolerance • Proficiency in data management and the use of office software such as spreadsheets, and word processing • Strong organizational skills, excellent communication skills, and report