287 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" positions at Stanford University in United States
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, perform desk-based computer tasks, use a telephone and write by hand, lift, carry, push, and pull objects that weigh up to 40 pounds. Rarely kneel, crawl, climb ladders, grasp forcefully, sort and file
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(REQUIRED): Advanced computer skills and demonstrated experience with office software and email applications. Demonstrated success in following through and completing projects. Excellent organizational skills
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files for immediate access to your resume, you must apply to http://stanfordcareers.stanford.edu and in the key word search box, indicate Requisition #108558 A cover letter and resume are required
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ability to quickly learn and master computer programs. Strong analytical skills and excellent judgment. Ability to work under deadlines with general guidance is essential. Excellent organizational skills
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and meet deadlines Ability to work cooperatively and maintain productive work relationships Basic computer skills and demonstrated experience with office software and email applications Other Relevant
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learn and master computer programs, databases, and scientific applications. Ability to work under deadlines with general guidance. Excellent organizational skills and demonstrated ability to accurately
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REQUIREMENTS*: Frequently stand, walk, twist, bend, stoop, squat and use fine light/fine grasping. Occasionally sit, reach above shoulders, perform desk-based computer tasks, use a telephone and write by hand
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*: Frequently stand, walk, twist, bend, stoop, squat and use fine light/fine grasping. Occasionally sit, reach above shoulders, perform desk-based computer tasks, use a telephone and write by hand, lift, carry
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Metabolism Postdoc Appointment Term: 2 years Appointment Start Date: Rolling admission, applicants can apply as soon as possible. Group or Departmental Website: https://www.research.va.gov/programs/bd-step
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due