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new knowledge and deliver next-generation predictive tools. These tools are meant to promote equitable policy interventions targeting extreme climate events, carbon dioxide emissions, and local air
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development and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users. Three strategic nanoscience themes underlie the CFN
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essential for progress on all aspects of this program. Required Knowledge, Skills, and Abilities: Ph.D. in experimental particle or nuclear physics or related field. Experience with high- and medium-energy
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the operation, software, and computing of the ATLAS experiment. Required Knowledge, Skills, and Abilities: Ph.D. in experimental particle or nuclear physics or related field. Experience with data analysis and
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the development of new radical scavengers for the conversion of radiolytic solvent radicals into secondary reductants/oxidants or unreactive species. Exploiting the knowledge gained from the above mentioned studies
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Knowledge, Skills, and Abilities: Candidates should have a Ph.D. in theoretical physics with demonstrated expertise in the area of high energy physics. Candidates should have demonstrated abilities in
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papers and presenting work at seminars and conferences. Required Knowledge, Skills, and Abilities: PhD in physical chemistry, or a related field. Preferred Knowledge, Skills, and Abilities: Experience in
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computer science knowledge. Preferred Knowledge, Skills, and Abilities: Practical experience developing novel AI/ML algorithms and models. Knowledge about hardware architectures, compilers, neural network
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rigorous wavefront simulations and AI/ML networks that account for the light-matter interactions in various wavelength regimes, and real light source parameters such as coherence, polarization
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proposals for ongoing research program Required Knowledge, Skills, and Abilities: Ph.D. in physics or related discipline within the last 5 years Strong background in condensed matter physics Data analysis