111 parallel-computing-numerical-methods research jobs at University of Minnesota in United-States
-
advanced training in teaching courses within the quantitative methods area. The training experience is intended to competitively position the Teaching Scholar for future employment in teaching-focused
-
of scientific research. Provide support to laboratory members and collaborators regarding study design, fixation methods, orientation and embedding methods, and special stains appropriate to the goals
-
prospect research information to help advance the University’s development program on the Prospect Development team. Prospect Development is a strategic department at UMF, focusing on providing research
-
analysis ●Assist with supervision of trainees and their interactions with study methods and data ●Contribute substantially to the preparation of manuscripts via literature review, data analysis and
-
experimental and computational methods for integrated multi-omics studies. About the Role · Lead independent research projects investigating somatic mosaicism in various tissue types of human or mouse models
-
statistical methods to agronomic research, including mixed models, geospatial statistics, multivariate analysis, and machine learning - Must possess and maintain an active and valid driver’s license Preferred
-
design and relevant analytical methods (5%) • Conduct data analysis and summarize results (40%) • Present the results at group meetings and prepare the abstract(s) for presentations at scientific
-
) and developing meaningful scaled score formulas and metrics. The post-doctoral associate will support multiple aspects of this work, including conceptualization and design of the scale scores, computing
-
Employee wellbeing program Financial counseling services Employee Assistance Program with eight sessions of counseling at no cost How To Apply Applications must be submitted online. To be considered
-
computational modeling of behavior to identify the underlying circuit computations. Current projects in our laboratory emphasize task-structured behavior assays, including set shifting, reversal learning