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Field
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Program teams to identify data needed to help them make key strategic decisions. Continuously improve internal processes with quantifiable data points. Contribute to collegewide data reporting and survey
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part of our research program outlined below. Our lab is in the Department of Biochemistry and Molecular Genetics and part of the RNA Bioscience Initiative at the University of Colorado School of Medicine
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, debugging), including CI/CD, containerization (Docker, Kubernetes), and robust debugging techniques. Experience with cloud platforms (AWS, GCP, or Azure) and parallel computing, with a focus on cost-efficient
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
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. utilities, libraries, plugins) Experience with parallel file systems (e.g. Lustre, GPFS) Experience in high performance computing (HPC) environment. Experience with configuration management software such as
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on the grants) and the IPD leadership team, you will ensure alignment across projects, monitor budgets at both project and program levels, and reallocate resources as needed to achieve overall program and
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.) Experience with big data tools (e.g., Hadoop, Spark, Kafka, etc.), data pipelines, and software development frameworks Experience with parallel programming (High Performance Computing experience is a plus) and
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/writing. They will also advise and mentor graduate students in the laboratory who will be conducting their own parallel but independent studies (5% effort), and will participate in departmental seminar, lab
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applications, forecasting and nowcasting, including precipitation and hydrological impacts. The Researcher series at SIO parallels the Professor series in terms of independent research and service, but it
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modeling using chemistry or materials codes, weather simulations, etc. Statistical modeling and data visualization Parallel software design with MPI, OpenMP, pthreads, or CUDA GPU computing environment Deep