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data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and concepts in both the area of molecular biology and the field
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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intelligence, machine learning, data science, applied mathematics, or a closely related field, awarded no more than three years prior to the application deadline*. Documented research experience in machine
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fields. Strong programming skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. Interest in Speech/Language Processing and its application. Know-how
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, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation
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advanced machine learning methods for multimodal and 3D medical image analysis in musculoskeletal medicine, in close collaboration with clinicians and computer scientists. PhD or Postdoctoral Researcher
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/or networked microgrids. Brief Description of Duties: The Postdoctoral Associate will assist the Principal Investigator Dr. Peng Zhang in the Department of Electrical and Computer Engineering . The
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, proteomics, metabolomics) and artificial intelligence/machine learning (AI/ML) applications in biomedical research will be considered a strong advantage. Outstanding U of A benefits include health, dental
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Monte Carlo, neural quantum states, tensor networks, machine learning and data science, dynamical mean field theory, diagrammatic Monte Carlo, etc.) Key Responsibilities Conduct independent and