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Lab applies rigorous evaluation and modeling methods, including natural and field experiments, randomized controlled trials, behavioral economics, and machine learning, to help policymakers identify and
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. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2025/phd-position-on-antimicrobial-materi… Requirements Research FieldChemistryYears
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at the atomic scale. The postdoctoral researcher will lead the theory component of a research program, developing microscopic models of impurity-host coupling and identifying optical signatures of correlated and
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or more of the following areas: + Quantum computing and quantum algorithms + Solid-state physics, computational materials science, or quantum chemistry + Battery materials modeling Excellent
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stem cell models that allow us to model disease and disease modifying factors. The successful candidate will be responsible for supervising graduate students, post-doctoral fellows, and undergraduate
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University of California, San Francisco | San Francisco, California | United States | about 8 hours ago
total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html Department Description The mission of the department of Community Health Systems (CHS) is to
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voluntary tax-deferred savings options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http
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cooperative research projects focusing on the recovery of the vital raw material phosphorus (P) from wastewater are being carried out, combining novel natural scientific and engineering approaches with
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will have the opportunity to learn and apply advanced data analysis techniques, including machine learning and econometric modeling. Through collaboration and technical expertise, this position supports
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driven inverse design of functional materials. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models