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Field
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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international members with 12 faculty and over 500 students in the B.S. and M.S. programs. Our interests span both research and practice, including artificial intelligence, big data, bioinformatics and
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-stakeholder research programs, portfolios, and projects supported by federal grants, philanthropic funding, and corporate-sponsored research agreements. Alongside scientific leadership (i.e., the PIs
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productivity while reducing external inputs. In parallel, the lab is expanding efforts to understand microbiome-associated phenotypes that contribute to drought tolerance and soil water retention. This includes
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advanced genome-editing tools. In parallel, we develop in-house sequencing technologies to dissect epigenetic and epitranscriptomic modifications at the molecular level, along with high-throughput platforms
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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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benefits package, generous paid time off, a competitive retirement savings plan, flexible work options, wellness programs including gym membership, professional development, tuition assistance, and more
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required. Five years of experience in an academic setting, preferably in an academic program, department, or school. PREFERRED QUALIFICATIONS: PhD or equivalent/parallel degree required. Seven years