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computational and theoretical methods to understand cluster and particle formation for atmospherically relevant molecules. ORCTOOL (Organic Cluster Tools) aims to create a toolbox for understanding and cost
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at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain. The position will be with Efstratios Gavves
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vision and depth perception as well as the ability to focus. WORK ENVIRONMENT: The individual in this position may be exposed to environmental conditions such as fumes or airborne particles up to 2/3
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relevance to extraterrestrial environments and future energy strategies. For more information on this project, see: https://copl.ethz.ch/research/research-projects/2025-saar.html Job description The PhD
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-Energy Theory at Harvard University Applicants are invited for research positions in high-energy theory (including but not restricted to particle physics, particle astrophysics, quantum gravity, quantum
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APT, TEM, FIM, EBIC, EBSD, XPS Kelvin probe microscopy, machine learning augmented analysis techniques) Experimental and computational analysis of transport and the reaction of surfaces and particles
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Foothill-De Anza Community College District | Los Altos, California | United States | about 1 month ago
: Environment: Indoor, office environment and outdoor environment, with climate changes. Hazardous conditions. Exposure to pollen, dust particles, chemicals and fumes. Includes travel to conduct work. Physical
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Texas. For more information go to: https://facilities.unt.edu/ Position Overview This position is responsible for the cleaning and sanitation work of the University, or on special projects
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of theoretical computer science researchers in Amsterdam (https://theory.amsterdam ). What are you going to do? You are expected to: carry out original research in the field of theoretical computer science; have
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particle formation for atmospherically relevant molecules. ORCTOOL (Organic Cluster Tools) aims to create a toolbox for understanding and cost-effectively predicting the rate of massively multicomponent