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cuantitativo. Capacidad para trabajar con grandes conjuntos de datos y entornos computacionales. / Experience in data science, data management and optimization, and machine learning and AI techniques. Strong
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application! Work assignments The project focuses on electro-enzyme coupled catalysis and reactor system optimization for sustainable energy and chemical conversion. The research will explore the integration
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optimization models for electric vehicle fleet charging planning. Activities: • Apply operations research methods (linear and mixed-integer programming, dynamic optimization, stochastic optimization) • Integrate
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innovative solutions for future communication networks and sensing-enabled services. Job description The successful candidate will contribute to research on energy-efficient network design, optimization, and
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; design and optimize synthetic strategies for complex peptides, peptide libraries, and small proteins; develop scalable production methods using solid-phase peptide synthesis and native chemical ligation
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Thin-film fabrication, processing optimization, and experimental troubleshooting Materials characterization using techniques such as GIWAXS, Raman, FTIR, UV-vis, and electrical transport measurements
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candidate will contribute to cutting-edge research in optimal control theory, differential game theory, mean-field game theory, and mean-field-type game theory, developing advanced mathematical frameworks and
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candidate will be expected to: Develop and optimize processes for the modification and nanostructuring of glass and optical material surfaces. Investigate relationships between fabrication parameters, surface
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Number req26782 Department Pharmacology and Toxicology Department Website Link https://www.pharmacy.arizona.edu/departments-directory/pharmacology-toxicology-department Location Tucson Campus Address
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Website https://seuelectronica.urv.cat/treballar-a-la-urv/pi26031english.htm Requirements Research FieldChemistryEducation LevelPhD or equivalent Skills/Qualifications PhD in Chemistry, Nanoscience