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, including RMC- and BCSA-based composites, (iii) studying carbonation and hydration mechanisms in these sustainable binders at the nanoscale using advanced in-situ characterization techniques, and (iv
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through the online application system ( https://www.leibniz-inm.de/en/job-offers-2/ ) Motivation letter CV (max 2 pages) Publication list (if available) Academic transcripts (B.Sc., M.Sc. and PhD) Contact
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Functional composites based on MULTIcomponent Portland cements modified with carbon-based nanomaterials: composition design, characterization of microstructure and properties (MULTIPHASE) number 2023/51/D/ST11
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monitoring stations on land and during incubation experiments). The analyses will involve the macroion composition (HCO3-, Cl-, SO42-, F-, Br-, NO3-, NO2-, Ca2+, Mg2+, K+, Na+, NH4+) as well as microscope
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the department We seek a candidate with the following qualifications : Required: PhD in wildlife ecology, conservation biology, or a related field Experience with technology-assisted wildlife monitoring (e.g
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). Biomass burning is an important source of aerosols and trace gases to the atmosphere and is a major
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. The candidates must hold a PhD in Chemistry/Physics. Experience in data framework development, kinetic/thermodynamic modeling, and collaborative interdisciplinary research. An education history in chemical
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composition and atomistic modeling of materials. The main activities will include: - formulating new descriptors of critical temperature (Tc) incorporating electronic fluctuation effects, evaluated by
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. The candidates must hold a PhD in Chemistry/Physics. Experience in data framework development, kinetic/thermodynamic modeling, and collaborative interdisciplinary research. An education history in chemical
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composition, production and performance. Improve existing datasets and create new ones useful for deep learning models Where to apply Website https://apply.interfolio.com/178410 Requirements Research