20 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of North Texas at Dallas
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Title: Postdoctoral Research Associate - Machine Learning & Advanced Manufacturing Employee Classification: Postdoctoral Research Assoc Campus: University of North Texas Division: UNT-Provost
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++, and Python. Preferred Qualifications Prior research experience in computer systems and distributed computing is preferred. However, strong research experience in developing/applying machine learning
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for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your work as part of our UNT
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/Certifications Job Duties: Perform computer simulation, perform experiment design and testing. Writing project reports, and grant proposals. Supervise and collaborate with graduate students in the lab. Publish
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/Certifications Job Duties: Perform computer simulation, perform experiment design and testing. Writing project reports, and grant proposals. Supervise and collaborate with graduate students in the lab. Publish
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in one or more of the following areas: computational biology/bioinformatics, biomedical informatics, deep learning/machine learning, data mining, biostatistics, or a closely related area. Proficiency
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in the best interest of our communities. We are encouraged to Be Curious about opportunities for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your
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. The Center is also home to the Structural Anatomy and Rehabilitation Sciences (STARS) PhD program that prepares graduate students to join the workforce as researchers and anatomy educators. The Center also
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. The Center is also home to the Structural Anatomy and Rehabilitation Sciences (STARS) PhD program that prepares graduate students to join the workforce as researchers and anatomy educators. The Center also
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-learning trained using the van der Waals corrected hybrid density functional theory (DFT) enabled SeA approach [J. Chem. Theory Comput. 19, 4182 (2023)]. The SeA approach is an accurate and efficient high