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, genomics, and other scientific domains. Hopper Fellows concentrate on the development and optimization of scientific and engineering applications leveraging high-speed network capability provided by
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with all elements of a multi-level research network, including attending Disease Team Committees (DTCs) meetings, interacting with sponsoring agencies including National Cancer Institute, and compliance
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. Preferred backgrounds include nonlinear optics, quantum optics, spectroscopy, or instrumentation. Proficiency in software and coding (e.g., MATLAB, LabVIEW, Python) is expected. Applicants with a strong
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real-time data analysis and interpretation code. Build a connection between experimental data, analytical solution and Monte Carlo X-ray scattering simulations. In collaboration with ALS Photon Science
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research focuses on a geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics
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dynamics, and bond valence. Strong coding skills including object-oriented programming, software engineering practice (version control, unit testing, continuous integration). Preferred language of experience
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geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants
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management and coding, machine learning AI, and familiarity with LSST data management image reduction and analysis tools. Familiarity with processing data at the DOE US Data Facility at SLAC National
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practices ● Fluency in programming languages such as R and Python. ● Demonstrated ability to make reproducible code for cleaning, integrating, and modeling spatial/temporal data from multiple sources, spatial
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to independently design and generate simulations using the Regional Oceanic Modeling System (ROMS) or other related codes (e.g., COAWST, CROCO, MITgcm, Oceananigans) in realistic and/or idealized configurations