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Field
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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, transcriptome and genome analysis software, Linux environment, HPC systems, TopHat, Bowtie, DESeq2, enrichment analysis tools, Genevestigator, psRNATarget, OmicsBox, etc.; experience using public omics databases
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to experimental data from photon-counting or time-resolved detectors. Experience with Bayesian methods, uncertainty quantification, or real-time data processing. Familiarity with distributed computing or HPC
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and risk assessment modeling. • Prior experience with CCUS research projects. • Familiarity with high-performance computing (HPC) and parallel computing techniques. Required Knowledge, Skills, and/or
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have experience with interactive visualisation of uncertainty (e.g., dashboards, uncertainty mapping). You have experience with HPC or parallel computing for computationally intensive tasks. Our offer A
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. outside of HPC environments) This post is subject to Disclosure & Barring Service and/or Occupational Health clearances.
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-series data of astronomical transients, including gravitational wave data analysis. Experience with high-performance or high throughput computing (HPC/HTC). The initial appointment is for 1 year. It is
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yield optimization. Exposure to quantum chemistry (DFT) and molecular simulations is a plus. Experience with cloud computing and/or high-performance computing (HPC) resources. Application Process
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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