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of administrative experience, OR a bachelor's degree. Proficiency with various personal computer software applications. PREFERRED QUALIFICATIONS: Knowledge of various computer software applications, and an ability
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implements data analysis pipelines for data science and machine learning / artificial intelligence (AI) to meet specifications of faculty or senior staff either independently or in collaboration with research
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personal computer software applications. Positions in this classification may require the ability to type or keyboard 60 accurate words per minute. ADDITIONAL JOB DETAILS: The Executive Assistant to the LGS
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research and policy. 3) Apply cutting-edge machine learning techniques using programming languages (R and/or Python) to develop predictive models and scalable data analysis solutions. 4) Prepare manuscripts
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experience with of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and can demonstrate real-world application of these techniques. MINIMUM
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collaborations tools (OneDrive, Zoom, Teams, and/or others). PREFERRED QUALIFICATIONS: Knowledge of various computer software applications, and an ability to draft and edit correspondence. Knowledge of fundraising
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bioinformatics, modeling, and machine learning to join our lab. Our research uses a multiscale approach to study the immune response to emerging/re-emerging viral infections. We study the dynamics of virus-host
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of equipment, ordering of supplies, inventory and media preparation. Maintains records, files and logs of work performed in laboratory notebooks and computer databases. Compiles data and records results
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media preparation. Maintains records, files and logs of work performed in laboratory notebooks and computer databases. Compiles data and records results of studies for publications, grants and seminar
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and priorities. Computer skills using Microsoft Office applications required as well as ability to learn and use program specific software and applications. Cover letter detailing relevant prior