46 parallel-processing-bioinformatics-"Multiple" Fellowship research jobs at University of Michigan
Sort by
Refine Your Search
-
methodology development as well as applied cancer bioinformatics in a variety of disease sites, including the incorporation of statistical, machine learning & QML ideas. Multiple collaborative opportunities
-
development in Neurodevelopmental disorders associated with altered chromatin regulation. The candidate will participate in multiple NIH-funded projects that explore disease mechanisms in rodent and human
-
. Among the methods used are high-throughput CRISPR screening, protein deep mutational scanning, massively parallel reporter assays, and genetic manipulation of cell lines and mice. The successful candidate
-
Apply Now Job Summary Dr. Con Ma?s lab in The Gilbert S. Omenn Department of Computational Medicine and Bioinformatics at the University of Michigan is seeking a postdoctoral research fellow to
-
research that together enhance our contribution to society. Responsibilities* Cong Ma's lab in the Gilbert S. Omenn Department of Computational Medicine and Bioinformatics at the University of Michigan is
-
, signal processing and image processing in medicine with an extensive level of access to large clinical datasets. Research in the Weil Institute focuses on the development, validation and maintenance
-
the project's multiple principal investigators (MPIs), the Post-Doctoral Research Fellow will contribute to the scientific aims of NSAL, including but not limited to: Assisting the MPIs and Project Director on
-
leading multiple research initiatives that leverage imaging and computational methods to address critical challenges in urology. Ongoing efforts include the development of dynamic contrast-enhanced
-
Medicine and Bioinformatics. The specific objectives of the project are to (i) deploy network analysis methods to genomic data (50%), and (ii) develop such algorithms including community detection algorithms
-
Medicine and Bioinformatics. The specific objectives of the project are to (i) develop mathematical and data analysis methods to analyze resilience, robustness, and tipping points of network dynamics, when