166 algorithms-phd-"INSAIT---The-Institute-for-Computer-Science" positions at University of Michigan
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. The intellectual depth, innovation and promise of the candidates for scholarly research are of high priority of this position. Required Qualifications* The candidate should have a PhD in Transportation Engineering
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in neuroprosthetics. This person will supervise ongoing animal experiments as well as experiments that are part of a related clinical trial. They will assist in mentoring PhD students to perform
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, file system implementations, I/O, deadlocks, distributed systems, synchronization, distributed file systems, case studies. Graded ABCDE CSC 575 - Algorithm and Complexity Analysis Graduate standing. (4
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actively seeking enthusiastic and innovative early-career researchers to join our Algorithm Core team. The successful candidate will engage in cutting-edge research in applied mathematics, focusing
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multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and their dynamics. Conducting literature searches, manuscript preparation, and
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Apply Now Responsibilities* Conduct research focused on designing and implementing algorithms related to cryo-electron microscopy (cryo-EM) data collection in a lab that focuses on the structural
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screening. Analyze experimental data and manage complex data science projects. Co-author publications and present results at meetings and to funding stakeholders. Required Qualifications* PhD degree and 5
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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
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results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms. Experience building web
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to synthesize sequencing-based analysis results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and statistical inference algorithms