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Field
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modeling. Perform predictive modeling using high-performance computing (HPC) infrastructure. Validate computational predictions by collaborating with experimental groups conducting reverse genetics studies
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involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations. The starting date is
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of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work provides the possibility to develop skills in
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local research software engineer for your research group(s) and improve the use of computing among all group members. Stay up to date with the latest HPC, AI, and general computing/data management
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-performance computing (HPC) environment Perform data analysis and visualization Perform machine learning and inverse design techniques Train and supervise masters and doctoral students Coordinate research with
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software and high-performance computing (HPC). These include particle and gravitational physics. -On the data analysis side, the group designs novel statistical methods for particle physics and astrophysics
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spatial distribution of critical topsoil properties in global drylands. Process large-scale geospatial and remote sensing datasets using High Performance Computing (HPC) systems. Conduct data analysis, and
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with high-performance computing (HPC) infrastructures is advantageous Excellent analytical and problem-solving capabilities Proven track record of publishing in reputable scientific journals and at
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months or 0.8 FTE for 45 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research environment within
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resistance and microbiomes, statistical analysis of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work