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architecture, concurrent systems, cryptography, databases, distributed systems, game theory, graphs, knowledge representation, logic, machine learning, networks, operating systems, programming languages
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University of California, San Francisco | San Francisco, California | United States | about 6 hours ago
for this position is $54,500 - $115,500 (Annual Rate). To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and
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Level 41 Salary Range $15.83-25.30/hour Type of Position Staff Position Time Status Full-Time Required Education BA Click here for more information about equivalencies: https://hr.uky.edu/employment
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technical contributions as an embedded software engineer for a wide range of projects requiring computing systems design and realization, including machine learning (ML) and artificial intelligence (AI
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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setting; and knowledge of learning theory. Preferred Qualifications: Master's degree in education or a related field. Three years of experience in academic student support or academic coaching in a higher
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to the student and administrative/faculty members as appropriate. Other duties as assigned by the program administrator. Basic knowledge of technology-mediated platforms for blended learning and computer software
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, KY Grade Level 09 Salary Range $21.00-34.29/hour Type of Position Staff Position Time Status Full-Time Required Education HS Click here for more information about equivalencies: https://hr.uky.edu
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data