290 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" positions at Stanford University in United States
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of experiments and outcomes. General computer skills and ability to quickly learn and master computer programs, databases, and scientific applications. Ability to work under deadlines with general guidance
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for immediate access to your resume, you must apply to http://stanfordcareers.stanford.edu and in the key word search box, indicate Requisition #108756. A cover letter and resume are required for full
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the future. Together, faculty and students in H&S engage in inspirational teaching, learning, and research every day. Department Description: The Red-Horse laboratory in the Department of Biology studies how
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the US to be considered for this position. Certifications and Licenses: None PHYSICAL REQUIREMENTS*: Constantly perform desk-based computer tasks. Frequently sit, sort, file paperwork or parts, grasp
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the US to be considered for this position. Certifications and Licenses: None PHYSICAL REQUIREMENTS*: Constantly perform desk-based computer tasks. Frequently sit, sort, file paperwork or parts, grasp
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. Substantial experience with MS Office and analytical programs. Ability to prioritize workload. PHYSICAL REQUIREMENTS*: Sitting in place at computer for long periods of time with extensive keyboarding/dexterity
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deliver reliable software with minimal supervision. Strong written and verbal communication skills in English. PHYSICAL REQUIREMENTS*: Constantly perform desk-based computer tasks. Frequently sit, grasp
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: Collaborate and participate in large consortia to assess needs and requirements Design and develop machine learning models for genomics applications Maintain and update existing deep learning frameworks
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REQUIREMENTS*: Sitting in place at computer for long periods of time with extensive keyboarding/dexterity. Occasionally use a telephone. Rarely writing by hand. * - Consistent with its obligations under the law
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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