69 parallel-computing-numerical-methods Postdoctoral positions at University of Minnesota
Sort by
Refine Your Search
-
scientists and scholars in those areas. Successful candidates will demonstrate how their mathematical and computational work makes substantive contributions to advancing these fields. Applications are welcome
-
-up Experience with microbial culturing, basic analytical methods (COD, N, P, OD600), and molecular biology techniques Ability to work and coordinate with external partners Ability to work independently and
-
-invasive measurements of oxygen levels within encapsulation devices containing insulin secreting cells, both in vitro and in vivo, using custom-built 19F-MR methods and equipment. Responsibilities: 90% MR
-
project on the bacterial phosphate uptake system. Protein biochemistry, enzymology, protein design, computational biology and structural biology will be key to these experiments. Translating some of these
-
researchers assisting with experimental work. Provide training in field methods, data collection, and research best practices. Support a collaborative and inclusive research environment. Data Management and
-
closely related fields · Experience and proficiency with CLM and/or other distributed hydrologic models, and a strong computational and programming background · Ability to work
-
slides for presentation. • Instruct students, fellows, and other inexperienced professional persons in proper laboratory methods and procedures. Qualifications Required Qualifications: • A Doctorate Degree
-
, Epidemiology, Computer Science, or related field -Highly qualified and motivated investigator (PhD, or MD/PhD) Preferred Qualifications: -Experience in statistical methods and analysis using SAS, R, or STATA
-
inexperienced professional persons in proper laboratory methods and procedures. Qualifications Required Qualifications: • A Doctorate Degree (PhD, which is completed within the last 3 years) in relevant
-
Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis