20 wireless-sensor-networks-postdoc Postdoctoral positions at Oak Ridge National Laboratory
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, HVAC systems, and building sensors and controls Expertise in whole building energy forward modeling (e.g., EPlus, OpenStudio, DOE-2), calibration and validation with empirical datasets Experience in
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Requisition Id 15490 Overview: The Multimodal Sensor Analytics group in the Electrification and Energy Infrastructure Division (EEID) is seeking a postdoctoral researcher with proven expertise in
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networked systems. It develops community applications, data assets, and technologies and provides assurance to build knowledge and impact in novel, crosscut-science outcomes. The position is supported by
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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across the laboratory. Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs. Postdocs: Applicants
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ecosystems. Strong communication skills and a collaborative mindset in cross-disciplinary teams. Special Requirements: Postdocs: Applicants cannot have received their Ph.D. more than five years prior
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on circular economy research Experience in working in the genetic algorithm and artificial neural networks is preferred. Experience in manufacturing process modeling of advanced manufacturing technologies
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (Transformers). A broad
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and