108 parallel-and-distributed-computing-"DIFFER" research jobs at University of Minnesota
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programs supported by the Regents Tuition Benefit Program Low-cost medical, dental, and pharmacy plans Healthcare and dependent care flexible spending accounts University HSA contributions Disability and
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outside Ability to learn to identify plants Valid driver’s license Ability to travel, including occasional overnight camping Preferred Qualifications: Computer literacy including MS Excel or other
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pharmacy plans Healthcare and dependent care flexible spending accounts University HSA contributions Disability and life insurance Employee wellbeing program Financial counseling services Employee Assistance
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medical and dental plans. In this position, you are automatically signed up for life insurance at no cost. We also offer a comprehensive wellbeing program; retirement plans; tuition benefits; leadership
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University HSA contributions Disability and life insurance Employee wellbeing program Financial counseling services Employee Assistance Program with eight sessions of counseling at no cost How To Apply
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, and nonprofit sectors. The incumbent will also (25%) support operations of the Center for Integrative Leadership, including administration of leadership development program outreach, communication, and
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, Karen, Oromo, and Somali. The supervisor for this position is Jacob Oertel, RIDGS Program Coordinator. Appointment Dates ● Fall Semester 2025 appointment dates are August 25 - January 7, 2026 ● Spring
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Assistant (RA) position. The RA performs research related to radiation therapy or imaging under the supervision of a Medical Physics Graduate Program faculty. The Medical Physics Graduate Program Director
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wages, paid holidays, and generous time off Continuous learning opportunities through professional training and degree-seeking programs supported by the Regents Tuition Benefit Program Low-cost medical
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic