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are gaining significant interest for sustainable seawater desalination. This postdoctoral position is part of a research project aiming to design, optimize, and evaluate electrochemical systems [electrodialysis
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data analysis/spectral image processing. Use of data analytics or machine learning to guide process design and optimization. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long
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, testing, and demonstration of novel CCUS technologies Process simulation, cost, life cycle, and social assessment of CCUS value chains Reactor design, optimization, and sizing using phenomenological and/or
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on mixture optimization and performance evaluation. Coordinate the use and maintenance of project-related equipment and materials in collaboration with laboratory managers in the CCEE department. Perform data
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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related field in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in
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. Methodology and innovation: Work with the PI to develop, optimize, and document novel experimental techniques and protocols to advance the lab’s research goals. Mentorship: Provide guidance and training
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resonance imaging (MRI) experiments in living rodent models to complete the work proposed in funded grants (CPRIT, NIH, other grants). Develops and optimizes scan sequences to enable quantitative in vivo MRI
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction
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Hadron Collider. The group currently plays a major role in the Phase 2 upgrade of the endcap calorimeter, the development of novel calorimeters for future experiments, the operation of the recently