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experience with the above methods and work with high-performance computing (HPC). Experience in analysis of spectroscopy data, spatial statistics, and data from soil or bacterial samples. Consideration will be
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-house CFD software packages. (3) Designing and developing CFD sub-models for application to a broad range of CFD problems. (4) Using high-performance computing (HPC) to accelerate complex, large-scale
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models (e.g., CNNs, diffusion models, etc) Proficiency in Python Experience with HPC (CPU or GPU, with GPUs preferred) Related Skills and Other Requirements Ability to collaborate on the application of AI
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consulting and data management services to UM6P researchers and their collaborators. UM6P has state of the art NGS sequencers and mass spectrometry and the largest HPC (High Performance Computing) cluster in
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. Excellent communication skills and ability to work in a multidisciplinary environment. Familiarity with cloud-based computing platforms (AWS, Azure, Google Cloud) and high-performance computing (HPC
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(HPC) international, multidisciplinary environment opportunities for further education and training Hospital-standard social benefits, e.g. jobticket UKF Your challenges: develop, implement, and apply
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, Python, Bash, and other programming languages for data analysis and statistical modeling. Automated Workflows Experience: Proficient in using HPC, Git/GitHub, Nextflow, Snakemake, or Docker for efficient
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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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disease research, especially involving multi-omic data integration Strong experience with network-based models, especially multiplex or multilayer networks, applied to biological data Familiarity with HPC
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or multilayer networks, applied to biological data • Familiarity with HPC environments is highly desirable • Programming skills in R, Python or equivalent • Excellent communication skills in English