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Code 8353NG Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The University of Minnesota’s Center for Magnetic Resonance Research (CMRR) (http://www.cmrr.umn.edu/) has
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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. For more information about the department, please visit https://cs.utexas.edu . Austin, the capital of Texas, is a center for high-technology industries, including companies such as Amazon, AMD, Apple
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for Artificial Intelligence. The position aims to strengthen methodological and algorithmic work on one or more of the following axes: Optimization and learning on very large models and datasets, improving
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Language Processing (NLP) and correlation algorithms applied to interaction data, metadata, and multimedia content, it ensures information integrity for both legal and regulatory compliance and the execution
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. With the support of machine learning algorithms and log analysis applied to traffic metadata and communication flows, it ensures system resilience for both legal and regulatory compliance as
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. Responsibilities Application of Domain Expertise: Maintain strong command of AI and machine learning concepts, including underlying science, math, and algorithms; stay current through reading publications, reviewing
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an interdisciplinary and international environment. Candidates should send their applications by April 30th through https://cv.newton-6g.eu/ Incorporations will begin in May 2026. DC1: Data-driven models for CF networks
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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/adaptive algorithms, offline and online data analysis, conducting experimental research, and online evaluation of the developed adaptive strategies with a robotic application. The prospective students can