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lattice field theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging
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into algorithms for the Lov sz Local Lemma and related problems in distributed and parallel models. Research will primarily be conducted in collaboration with Dr. Davies-Peck, but successful applicants will also be
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algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for parallelism of the completion process, highlighting
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is also free to propose a personal research path to be followed in parallel if it aligns with the general topics of interest of the division: solar and heliospheric plasmas in the Newtonian regime, and
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into algorithms for the Lovász Local Lemma and related problems in distributed and parallel models. Research will primarily be conducted in collaboration with Dr. Davies-Peck, but successful applicants will also be
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with ab initio methods. Proficiency in software such as VASP, Quantum ESPRESSO, Critic2 and Gibbs2. Experience in high-performance computing and parallel processing. Additional Qualifications Considered
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platform enables us to test hundreds of different conditions in parallel and assess their impacts on human immune responses, such as antibody production. We routinely work with industry partners to exploit
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development. Knowledge of parallel scientific computing. Ability to meet project needs and tight deadlines. Present and publish results in peer reviewed papers and/or journal articles. Skilled verbal and
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techniques. In parallel, we are also looking for postdocs interested in developing high-throughput screens for single-domain antibodies, called nanobodies, that perturb intracellular biology directly at the
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive