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machines are in proper working condition and implement a preventative maintenance schedule. Knowledge, Skills, and Abilities: Thorough understanding of various welding processes (MIG, TIG, Stick, flux-core
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. Skill with computer & data collection systems such as student information systems (Peoplesoft, Salesforce), Microsoft Office Suite, Adobe Acrobat Pro DC, etc. Detail oriented, self-motivated, and highly
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of advanced MRI processing pipelines comprising 1) k-space data manipulation, 2) MRI artifact correction, 3) integration of multi-contrast and longitudinal MRI data, 4) statistical and machine learning
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effective and productive working relationships with on-campus and off-campus partners. Ability to interact with varied internal and external partners on all levels. Computer skills and knowledge of Microsoft
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, or a related field. At least six years of teaching experience in person at the college or university level, including at least one year of teaching business analytics/applied statistics/machine learning
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environment and have excellent written and verbal communication skills. Also, the incumbent should be able to maintain sensitivity with confidential information, and possess computer proficiency in Microsoft
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: Knowledge of NetSuite or other inventory management systems. Basic computer skills including Excel and Microsoft Suite. Knowledge of shipping systems. Ability to work occasional nights/weekends. Physical
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Abilities: Ability to communicate effectively, both in writing and verbally. Ability to use a computer to send/respond to email (Microsoft Outlook), complete time sheets (Microsoft Excel & Beebole), fill out
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email etiquette. Strong organizational and time management. Ability to learn new media systems and respond quickly to constantly changing technology. Strong customer service philosophy; including
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effectively in a professional manner and is a team player. Physical Requirements: Ability to lift up to 50 lbs. Minimum Qualifications High school diploma or equivalent, or equivalent learning attained through