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to produce cutting edge social science, computer science, and data science research that improves public policy decision making. The MDI Machine Learning Developer will engage in research, development, and
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machine learning pipelines for protein design, including data preprocessing, model training, and validation protocols Analyze protein structure-function relationships using computational tools and databases
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Diploma (or equivalent) required, college degree preferred At least 3 years, preferably more, of security related / law enforcement / military experience and community policing An equivalent combination
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non-emergency calls; monitor phone and radio traffic to ensure procedural compliance; review case information entered into the computer system; conduct quality assurance feedback and coaching sessions
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, communities, and across campus. Living in the assigned residential area, work primarily in an office or desk workspace using a university issues laptop computer; Some meetings or events in other buildings
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(TSSDS) maintains and operates the Physics machine shop at Georgetown University and provides technical support for research and teaching to the department of Physics, as well as to other science
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constituencies; Demonstrable aptitude for learning new computer-based skills and for sharing knowledge with team members; Excellent interpersonal and communications skills; Familiarity with all standard tools
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because this is the place where theory and practice meet. At Georgetown, students learn the law in the place where laws are made. The Event Coordinator supports room scheduling and administrative functions
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, computer science, or bioinformatics Programming skills in Python is required Preferred requirements include experience in omics data analysis and machine learning Work Mode Designation This position has been
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. Communicate effectively, both orally and in writing. Ability to foster a cooperative work environment. Valid driver’s license with good driving record; must be insurable. Proficient in computer use and relevant