17 molecular-modeling-or-molecular-dynamic-simulation Postdoctoral positions at Brookhaven Lab
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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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to investigate the molecular basis of plant-pathogen interactions in bioenergy Sorghum. The successful candidate will study plant immune responses at the organismal, subcellular, and molecular levels to uncover
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push the envelope to accomplish our missions, we develop novel tools and techniques in biochemistry, molecular genetics, structural biology, cell biology, plant pathology and bioimaging that also benefit
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, and Abilities: Experience with neutron or x-ray scattering from single crystals Experience with characterizing magnetic and structural dynamics using neutron scattering Modeling neutron scattering from
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investigators. Position Requirements Ph. D. in theoretical or physical chemistry, or a related field Extensive experience in one or more of the following areas: Computational modeling of homogeneous
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transport modeling and machine protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses
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simulation and optimization of electron beam spin polarization in the electron storage ring (ESR) of the Electron-Ion Collider. This includes expanding the spin tracking capabilities of the accelerator design
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deployment strategies to evaluate and inform next-generation predictive urban climate models (e.g., using OSSEs (Observing System Simulation Experiments) or ablation studies, through extensive literature
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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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optimization. Basic knowledge of integrated circuit design, including digital simulation and logic synthesis. methods, and other related topics pertaining to fast AI model inference. Experience working in