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Post Doctoral Researcher, Electrical and Computer Engineering Apply now Job no: 501817 College / VP Area: College of Engineering Work type: Staff Location: Newark, DE Categories: Research
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data analysis and work with public and private data sets (e.g., O*NET and Lightcast). A computer science background and the ability to build data pipelines are a plus, as well as fluency in modern AI
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data analysis and work with public and private data sets (e.g., O*NET and Lightcast). A computer science background and the ability to build data pipelines are a plus, as well as fluency in modern AI
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Position Title: Postdoctoral Research Associate in Electrical and Computer Engineering Appointment Type: Post Doc/Trainee Job Description: Summary of Duties and Responsibilities: The Department of
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18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc position in 3D Computer Vision, remote sensing ... The Laboratory of Visual
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Show all breadcrumbs Postgraduate Associate (Computer Science and Neuroinformatics—Cho Lab) Friday, September 26, 2025 Postgraduate Associate (Computer Science and Neuroinformatics—Cho Lab). The Cho
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The department of computer science (DIKU) invites applicants for a 24 month postdoctoral fellowship in stochastic models of morphology evolution. The postdoc will perform research in relation to the
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Computer Science may be found at https://cdsp.wm.edu/computerscience/ . Posting Range: $70,000 - $75,000, commensurate with experience and internal alignment Additional Job Description: Job Profile
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the curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 14 hours ago
focuses on algorithm development, data fusion, and deployment for anomaly detection in solar panels and battery systems. Key Responsibilities Develop computer vision and deep learning algorithms