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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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Start Date: Preferably by September 2026 Group or Departmental Website: https://fireecology.stanford.edu/ (link is external) How to Submit Application Materials: Please submit all application material
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Institute for Medical Engineering and Science at MIT | Cambridge, Massachusetts | United States | about 12 hours ago
) Save job Click to add the job to your shortlist Send job Job Details The Department of Electrical Engineering and Computer Science (EECS) together with the Institute for Medical Engineering and Science
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Resources at 479.575.5351. Department: Graduate & Hourly Employees Department's Website: EECS Department Website: https://engineering.uark.edu/electrical-engineering-computer-science/ Artificial
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Job Type/Hours per Week Full-time/40 Hours per Week Salary Commensurate with experience. Hiring Department Department of Electrical and Computer Engineering Location University of Texas at Tyler
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, sex, sexual orientation, gender identity, national origin, citizenship, age, disability or protected veteran status. Required Degree Doctorate -Electrical and Computer Engineering Minimum
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for Integrative Genomics The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, headed by Laura
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: We invite applications for multiple Postdoctoral Fellow positions as part of the NSF AI Institute for Human-AI Cooperation (NSF HAIC) ( https://haicinstitute.org/), a $21.5 million multi
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., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision, tracking, spatio-temporal modeling, edge learning