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device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
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experimental methods, and high-quality research outcomes. Key Responsibilities Plan and execute radiochemical laboratory experiments involving radiotracers, irradiated targets and materials, and aqueous
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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, characterizing mass transfer and selectivity under flow conditions, and screening and tuning DES compositions to optimize solubility, speciation, and electrochemical accessibility for target elements
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University of Notre Dame to develop novel on-chip superconducting quantum detectors with magnetic targets for low-energy axion dark matter detection. Key Responsibilities: Develop superconducting hybrid
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focusing on energy technologies. Collaborate on the creation of a comprehensive supply chain database specifically for targeted energy technologies. Apply advanced analytics to assess and classify domestic
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innovative therapeutic strategies for targeting IDPs, including biologics such as protein-protein inhibitors, Proteolysis-targeting chimeras (PROTACs), nanobodies, and more. Key Responsibilities: Develop
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current sensors) Develop and characterize superconducting nanowire single-photon detectors (SNSPDs) using high kinetic inductance materials such as NbN, TiN, and NbTiN, targeting high detection efficiency