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experiment at the EIC. The program includes data analysis involving polarized targets at Jefferson Lab as well as full detector and physics simulations for ePIC. In addition, the candidate will collaborate
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will collaborate closely with NSLS-II staff while developing cutting edge sample preparation and data analysis techniques that enable the next generation of the XCFS methodology. In addition
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complex terrain regions. CMAS does this by innovating on the fronts of meteorological data acquisition, analysis, and interpretation (https://www.bnl.gov/cmas/). The CMAS work portfolio is conducted within
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a related field Experience with radiation transport codes (e.g., FLUKA, Geant4, MCNP etc.) Excellent programming and data analysis skills (e.g., Python, C++, or similar) Solid understanding
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to perform quantitative spectroscopic analysis using monochromated EELS or other advanced spectroscopy techniques Preferred Knowledge, Skills, and Abilities (one or more of the following): Background in
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of gas chromatography coupled mass spectrometry or Liquid chromatography-mass spectrometry analysis. OTHER INFORMATION: Candidates should submit a CV, a brief description of how their research experience
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., EBL, physical vapor deposition, ALD etc.) and critical dimension (CD) measurements & analysis using scanning electron microscopy (SEM) and CD analysis software. You communicate effectively, verbally and
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artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis of the big data acquired by electron microscopy. • You work
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses
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computational resources for data analysis. This position offers a dynamic, collaborative environment, engaging with experts across plant biology, microbiology, structural biology, and computational sciences and