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Field
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formats as appropriate). Analyzes and integrates multi-modal genomic datasets using computational approaches (with access to robust HPC resources). Evaluates data to plan subsequent stages of projects and
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of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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on HPC computer architectures and deliver them as user-friendly software to meet DOE experimental facility needs. We’re here for the same mission, to bring science solutions to the world. Join our team and
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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Proficiency in R or Python Minimum of two years of experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer
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: · Mammalian cell culture experience. · Experience with high-performance computing (HPC) environments. · Experience in UNIX and Python/R · Exposure to reverse genetics systems or viral
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., DAS, broadband networks). Proficiency in open-source, version-controlled environments (Python, git, conda). Comfort with cloud/HPC workflows in a Unix-based environment. Preferred Qualifications
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wind tunnels such a temperature/pressure sensitive paints, infrared thermography, PLIF, FLDI etc. • Proficiency in Python • Experience running simulations with high performance computing (HPC) resources
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with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must be submitted electronically and include a cover letter, statement of
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific