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dynamics in models Present results at scientific meetings and prepare manuscripts for peer-reviewed publications Travel to the field site in North Dakota on occasion and when needed For more details on the
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to solve challenging problems in the microelectronics area. Note: Synthesis of bulk materials, first-principles simulations/modeling, and organic or bio-related areas are not in consideration
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(electrochemistry, materials synthesis, or characterization) or computational simulations perspective, is required. Proficiency in Python programming is required. Familiarity with REST APIs is desirable. Master’s
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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candidates with a strong theoretical background in one or more subfields including topological materials, strongly correlated quantum systems, quantum dynamics, and quantum information theory. A particular
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researchers performing molecular modeling and machine learning activities. The candidate will be expected to perform ion conductivity experiments with thin film polymer electrolytes. The candidate will use
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pipelines and methods for neuroimaging. The successful candidate will join a dynamic team of scientists conducting a wide range of research with state-of-the-art tools. The IMG operates three beamlines
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including engineering, economics, and environmental science. Experience developing mathematical or computational models for simulation and optimization of energy/economic systems in ASPEN Plus® and/or Julia
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into such questions, with an emphasis on exploiting x-ray wavefront coherence at short wavelengths. Areas of focus will include quantification of lattice quality and growth dynamics during chemical vapor deposition
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cosmology, and simulations. Analysis efforts cover topics such as CMB power spectra, CMB lensing, galaxy clustering, redshift-space distortions, and weak and strong gravitational lensing. The observational