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Field
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Learning and Deep Learning libraries (e.g., Pytorch, Tensorflow) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Laboratory for Computer-Human Intelligence (CHI-lab, https://www.x-labs.xyz ) in
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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FieldEngineeringEducation LevelPhD or equivalent Specific Requirements Position Specific Required Qualifications: PhD in Computer Science, Electrical and Computer Engineering, Data Science or any other relevant field
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research should they wish it. Bucknell has numerous opportunities to both learn how to be an effective teacher and to teach courses and labs both in the electrical and computer engineering department and
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description The successful
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campus in Lemont, Illinois five days per week. Preferred Qualifications Proficiency in programming (e.g., Python) for advanced data analysis, machine learning, and computer vision to accelerate insights
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to these values. We are seeking an excellent candidate for a Postdoctoral Research Associate in 3D Deep Learning for Vision and Lidar position who has: a PhD (or near completion) in a relevant field background