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Field
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in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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conducting lifetime modeling, developing advanced condition monitoring techniques, and applying data-driven analytics for lifetime prediction. You will play a central role in integrating experimental insights
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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SPECIFICS Postdoctoral Researcher in Defect Modeling The Pennsylvania State University Department of Nuclear Engineering The Computational Nuclear Materials Group (https://sites.psu.edu/cnmg/ ) at Penn State
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accessible, community-driven technology solutions. Research Leadership Responsibilities The post-doctoral researcher will: Lead development of cross-modal embedding alignment frameworks Design and implement
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advances in 3D vision, AI-driven detection, and multimodal sensor integration (RGB, RGB-D, LiDAR), and digital twins. The postdoctoral fellow reports to Dr. Vedhus Hoskere in the College of Engineering/Civil
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, or industrial engineering. Proficiency in Python and simulation tools (e.g. AnyLogic, Arena) and optimization solvers (Gurobi, CPLEX). Solid background in decarbonization modeling, logistics, and techno-economic
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Posting Title Postdoctoral Researcher – Enzyme engineering / Chemoenzymatic synthesis . Location CO - Golden . Position Type Postdoc (Fixed Term) . Hours Per Week 40 . Working at NREL The National
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for pediatric diseases. Our research focuses on how genetic variants contribute to diseases during early development and on developing mechanism-driven therapeutics. While animal models have advanced our