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Field
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Collaboration. The primary focus of this post will be the development of computational pipelines for the automated extraction and discovery of image-derived phenotypes (IDPs) across multiple imaging modalities
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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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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safety-critical tasks, such as healthcare or automated vehicles, where trustworthy models are required. The existing literature for the OoD detection problem focuses on the development of confidence scores
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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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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
supporting multiple probes simultaneously. Swarms also provide the usual benefits of multi-element array reception, namely robustness to single point failures and transmit/receive diversity. The downside
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University of California, Los Angeles | Los Angeles, California | United States | about 15 hours ago
optimizing algorithms, supporting HPC- and data-intensive workflows, and contributing to the development and evaluation of emerging technologies in high-performance computing, data analytics, and
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. The successful candidate will lead the computational efforts of developing and applying methods for applying proteomics and genetics data collected in situ for integrative structure modeling. Critical aspects
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop