39 parallel-computing-numerical-methods Fellowship positions at University of Michigan
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. 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
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techniques, methods, and research, especially deep learning literature, and how these methods apply to our use cases Ability to manage multiple projects and assignments with a high level of autonomy and
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research experiments, determining the most suitable methods to be used. Interpret and perform complex data analysis on experimental results and determine whether they are consistent with goals. Maintain a
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learning, teaching, and scholarship through application of technologies and knowledge of the latest digital research methods with diverse teams. You will join a network of functional and subject experts
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cell or drug therapies. We are interested in recruiting bioinformatics research fellow with training in computational biology or biostatistics. relatstatistics, mathematics, or a related field.statistics
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applications. Our research program consists of two main thrusts, 1) the development and application of state-of-the-art laser and optical sensing techniques - both on the ground and in flight - to better
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consult with our multi-disciplinary team of scientists, developers, and clinicians. Write manuscripts to summarize the research findings Be an expert on the latest machine learning techniques, methods, and
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waters specifically; Develop research questions, research designs, and appropriate methods for conducting empirical investigations related to community resilience and transboundary waters; Develop
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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
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Apply Now Job Summary The hired postdoctoral researcher will perform independent and directed research to achieve the objectives of the research project within the Department of Computational