79 computational-materials-physics Postdoctoral research jobs at University of Minnesota
Sort by
Refine Your Search
-
spending accounts University HSA contributions Disability and life insurance Employee wellbeing program Financial counseling services Employee Assistance Program with eight sessions of counseling at no cost
-
teaching methods to science or mathematics teaching at the college level • Masters in a science (e.g., biology, chemistry, chemical engineering, physics) or mathematics field or equivalent • Experience with
-
DeComposition or other similar process based crop and soil models Preferred Qualifications Post-doctoral experience related to soil carbon science and accounting and a demonstrated ability to independently lead
-
design and execution 35 % research 30% presenting and writing about research Other duties of a similar scope as assigned. Qualifications Required Qualifications: Ph.D. in Physics, Applied Physics
-
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 Applications must be
-
collaborates with other fields such as chemistry, computer science, engineering, linguistics, mathematics, medicine and allied disciplines, philosophy, physics, and psychology. The Department of Neuroscience is
-
insurance 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
-
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
-
to improve your biocomputing skills through tutorial Qualifications Required Ph.D. in Computational biology, Bioinformatics, Plant breeding/genetics, or related field. Individual may be immediate post-degree
-
to their research. Ideal candidates will be expected to employ state-of-the-art computational tools to analyze phylogenetic, metagenomic and multi-omics data sets generated from different clinical trials. Applicants