312 parallel-processing-bioinformatics-"https:" positions at Oak Ridge National Laboratory in United States
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impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. You will work in a collaborative research and development environment
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systems. Expertise with batch schedulers (SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Proven ability to lead technical projects from concept through implementation, balancing
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in evolutionary biology, plant biology, genomics, bioinformatics, mathematics, statistics, computer
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of the trained models to determine underlying processes that govern the given data. This job offers an excellent opportunity to conduct exceptional and innovative research in mathematics, statistics and scientific
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enrichment devices for processing uranium-bearing and stable isotope compounds. The Mechanical Systems Modeling Group applies first-principles physics and empirically informed methods to advance
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environment consisting of mathematicians, computational and computer scientists, and domain scientists conducting basic and applied research in support of ORNL’s mission. Specific responsibilities include
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. https://jobs.ornl.gov/content/Benefits/?locale=en_US Relocation: Moving can be overwhelming and expensive. UT-Battelle offers a generous relocation package to ease the transition process. Domestic and
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process