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models using software such as GAMS and Python. Econometric modeling skills using statistical software (e.g., Limdep, Stata, EViews or R). Strong programming skills with R and Python. Excellent oral and
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Scholar/Fellow positions is to provide individuals, who have recently (within the past 6 years) completed a doctoral degree, with a full-time program of advanced academic preparation and research training
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completed within the last 5 years in computer science, data science or a discipline related to the job duties. Experience in parallel computing. Experience with storage systems. Previous research experience
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generative design tools such as HEEDS, and/or the Dakota or RAVEN uncertainty quantification tools. Experience with FORTRAN, C, and/or C++ applied programming. Knowledge of Python, Java, or other scripting
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package, including health and life insurance, generous paid leave and retirement programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https
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sets and backgrounds are therefore also critical. Additional desired skills include the following: experience using ROMS; familiarity with parallel computing and high-performance computing environments
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acquisition (DIA), data dependent acquisition (DDA), and parallel reaction monitoring (PRM) proteomics experiments to fit the specific experimental needs of stakeholder cancer researchers. Manage a wide variety
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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on advancing agricultural food technology through the integration of artificial intelligence (AI). Specifically, the Postdoctoral Scholar is expected to conduct intellectual research involving parallel and
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, Bioinformatics, Computer Science, Mathematics, Statistics, Data Mining, Parallel Programming, Supercomputing, or Cloud Computing. 3) Experience collaborating with diverse and geographically distant teams