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to humans, plants, and beneficial organisms. Knowledge of and/or ability to learn UC and California Department of Pesticide Regulation laws and regulations, pesticide labels, SDS, manuals, and policies
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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or human-computer interactions studies. The focus of the PhD-thesis needs to contain knowledge areas such as learning theory, cognitive theories with applications on studies of learning, design of learning
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; data analysis; modeling; statistics and machine learning; scientific simulation; or theoretical astrophysics. COMPENSATION AND BENEFITS The full-time annual compensation for this position is $95,000. In
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, data processing pipelines, machine learning and generative AI applied to physical activity and sleep research. The position will contribute to the development of an AI-based behaviour change tool and
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science, artificial intelligence, machine learning, computational social science, data science, or a related computational discipline. Applicants must have experience with digital trace/multimedia datasets and in
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interpretable machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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. Strong experience integrating EEG with non-invasive electrical brain stimulation, particularly tRNS. Strong experience investigating individual differences in learning and cognition Experience applying
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fellow to join the multidisciplinary research team of Dr. Akhilesh Pandey at Mayo Clinic, Rochester, working at the intersection of digital pathology, artificial intelligence (AI), and machine learning