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Field
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-fidelity optimization, neural architecture search, or large-scale AutoML systems. Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific
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methods with optimization and decision-support models. Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis
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; • Familiarity with statistical analyses and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods; • Experience working with large-scale datasets
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of the produced powders and films: SEM/EDS, TEM, XRD, ICP-OES, EBSD, etc. Correlate material morphology and composition with final TE material and device performance. Develop TE devices optimized for both energy
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alongside guidance from senior research personnel. Key Responsibilities • Experimental Design: Develop and optimize behavioral and physiological paradigms targeting brain networks. • Data Collection: Conduct
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cartilage and intervertebral discs under biomimetic loading conditions, coupled with advanced imaging systems. - activities: Adapt and optimize the SOLANO miniaturized hexapod robot (Symétrie) for high
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to provide scalable, faster-than-real-time capable methods and tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems. Your Job This postdoctoral position
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computational chemistry techniques and data-driven approaches to optimize the properties of novel polymer-based materials. The ideal candidate should have a strong background in artificial intelligence and
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network optimization for 6G/FutureG wireless networks especially on the Internet of Intelligent Things under the supervision of Dr. Lingjia Liu (https://lingjialiu.ece.vt.edu). Successful candidates will
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leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy resources, demand response, and storage, it aims to enhance grid flexibility and