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. Numerical Analysis and Scientific Computing, developing numerical methods and algorithms that are accurate, efficient, and robust with respect to challenges that manifest in the simulation of complex
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, integrating five innovations. First, you will establish material accountancy algorithms using Monte Carlo simulation and Bayesian inference to distinguish normal tritium losses from suspicious discrepancies
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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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Description The Institute for Computer Science, Artificial Intelligence and Technology (INSAIT ), created in partnership with Switzerland’s ETH Zurich and EPFL, is seeking candidates for multiple faculty
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from multiple systems and sources to answer key operational questions. Deep experience using a variety of data mining/data analysis methods to build and implement dashboards, models and algorithms
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hypothesis testing to assess the validity of a set of candidate thresholds. Therefore, the internship position aims to extend this work by exploring one or multiple of the following directions of improvement
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sampling-based and reinforcement learning-based motion planning algorithms for multiple robotic arms in automotive manufacturing, including testing, performance evaluation in both simulation and actual
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, that will drive research and innovations at multiple levels - from developing scalable quantum processor technologies to solutions for the quantum-classical control and readout interface, and all the way
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to test and evaluate SONAR signal processing and automation algorithms developed both internally and externally (may include travel to test sites for specific test events). Implement research grade
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. The candidate must have experience working within a team environment, be able to work on multiple projects simultaneously, and work well under pressure to meet deadlines. The candidate must be proficient in query