80 parallel-and-distributed-computing-"DIFFER" Postdoctoral positions at University of Minnesota in United States
Sort by
Refine Your Search
-
Certificate Program for graduate students in speech-language pathology and audiology. Faculty research and teaching are strongly supported at the unit and collegiate levels. Interdisciplinary collaborations
-
plasticity metrics derived from functional MRI data. Investigate developmental differences in infant brain functional networks Support generation and testing of improvements for code bases for the analysis
-
teaching the same courses in different semesters. Final teaching will be determined in consultation with the Associate Chair for Curriculum & Instruction. The incumbent will also engage in training and
-
. Expected distribution of duties includes: ● Laboratory benchwork: 75% ● Data analysis, writing, and presentations: 25% Qualifications Required Qualifications: ● A PhD degree in Neuroscience or a related
-
) to promote nitrogen BMPs and sound nitrogen management in crop production - Secure external funding for research and outreach as relevant Job Duties/Time Distribution: (70%) Analyze current data sets related
-
Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job The Hulleman Laboratory is looking for a motivated and talented postdoc for a two-year program to focus on an exciting
-
pharmacy plans Healthcare and dependent care flexible spending accounts University HSA contributions Disability and life insurance Employee wellbeing program Financial counseling services Employee Assistance
-
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
-
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
-
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