77 parallel-and-distributed-computing-phd Postdoctoral positions at University of Minnesota
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progression toward research independence. Responsibilities: 45%- Develop Species Distribution Models for Invasive Mystery Snails Develop and implement species distribution models (SDMs) for Cipangopaludina
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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: A recent PhD (or MD) degree in a relevant discipline, such as decision science, health services research, epidemiology, applied mathematics, or industrial engineering. Expected graduation in Spring
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closely related fields · Experience and proficiency with CLM and/or other distributed hydrologic models, and a strong computational and programming background · Ability to work
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or publications. Qualifications All required qualifications must be documented on application materials. Required Qualifications PhD in biological science and/or bioinformatics or computational biology. Knowledge
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results, and discussions of possible new approaches with team members and the Principal Investigator. Qualifications Required Qualifications: PhD in Statistics, Biostatistics, Computer Science, Electrical
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: Applicants must hold an MD and/or PhD degree in a biological science. Must be willing and able to handle mice, human blood products and lentiviral transduction of human cells. Desired: Preference will be given
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(PhD, which is completed within the last 3 years) in virology or immunology. • Previous lab experience working with respiratory viruses such as influenza virus. • Experience working with human clinical
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environment, to assist trainees, and present their work in shared lab meetings and external meetings. Highly motivated individuals holding a PhD degree, preferably in virology, are encouraged to apply. The lab
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%) Code in Matlab or other program for stimulus generation, presentation, and analysis, as well as data analysis. (25%) Prepare data for publication and presentation via peer-reviewed journal articles and