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. The successful candidate will be a key contributor to a multidisciplinary co-design team spanning material science, computing, and electronic engineering, with the goal of enabling next-generation detector
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into photonic or electronic systems Hands-on experience with SEM, AFM, TEM, and other characterization tools Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
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emphasize multi-wavelength survey science, the galaxy-halo connection, cluster cosmology, and large-scale cosmological simulations. Analysis efforts cover topics such as CMB power spectra, CMB lensing, galaxy
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machine learning models at a world-class high-performance computing facility The candidate will have access to state-of-the-art computing resources, including: NVIDIA DGX-2 Systems: Powerful platforms
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
). Proficiency in scientific programming (Python, MATLAB, or equivalent). Ability to work effectively in a multidisciplinary, multi-institutional collaboration. Excellent written and oral communication skills
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relationships in next-generation electronic materials. This role involves creating AI models for real-time data analysis, enabling autonomous experiments through active learning and "curiosity-driven" exploration
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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collaboration with team members. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Computer skills relevant for data
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team and contribute to IDEAS (Intelligent Data Exploration Assistant for Science), a multi-year research effort focused on developing AI-powered visualization systems that enable interactive, human-in