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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 3 hours ago
of materials Proficiency in multi-physics FE simulations, topology optimization, and working knowledge of machine learning (ML) algorithms Demonstrated exceptional problem-solving abilities, a passion for
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theoretical foundation. We are looking for a candidate excited by the challenge of rethinking data systems in the face of modern constraints such as optimization under uncertainty, constrained query evaluation
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). Mission The recruited researcher will carry out research for the AgriBioPack project: Valorizing Agri-food Residues for Bio-based Packaging Solutions. Activities • Optimize extraction and separation
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. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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qualified Postdoctoral Research Associate. The incumbent will join a collaborative team to combine synthetic magnetosome biogenesis and computationally optimized cobalt-binding peptides to enable efficient
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involves the development and optimization of both in vitro and in vivo models to investigate diabetes-related disease mechanisms and to evaluate potential therapeutic targets. Working under the supervision
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environments. Working closely with partners in the NEXPECH₂ project, you will analyze their materials and provide valuable insights to help optimize device performance. Additionally, you will support
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validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous computing where the focus will be to work on to the current efforts
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into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design