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work will be informed by collaborations with the scientific computing team that manages the local HPC resources at NTNU, and Uninett Sigma2, which manages both the Norwegian national infrastructure
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pipelines in R, Python and HPC or cloud environments. Collaborate with developmental biologists to interpret genomic findings, validate results and address key evolutionary questions. Support and mentor PhD
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CRC2 & Mitchell Funded Tenure-Track Faculty Positions in Supercomtuping and High-Performance Computi
Queen's University - Electrical and Computer Engineering, Smith Engineering | Kingston Downtown, Ontario | Canada | about 11 hours agolibraries (e.g. SCR, Adios, HDF5 and concepts e.g., POSIX). • deep expertise in one or more supercomputing/HPC research areas. Examples of possible expertise (not an exhaustive list) include: o
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, HPC, storage, security, and advanced computing teams to understand scientific workflows and translate requirements into sustainable workstation solutions. Support installation, validation, and
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, chemoinformatics and scientific programming is required, alongside familiarity with machine learning and the ability to work independently and collaboratively. Experience with Linux/HPC environments, machine
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Earth system modeling framework remains technologically advanced and capable of efficiently exploiting emerging HPC architectures. Emphasis will be placed on heterogeneous computing and the optimal use
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of reproducible research methods and software version control (e.g. git/GitHub). • Experience using high-performance computing (HPC) environments and job scheduling. About the Department The Department
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experience with advanced methods of theoretical chemistry. Knowledge of and practical experience working in an HPC environment. Knowledge of programming in Python. Experience in processing 4D-STEM datasets
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description Are you a highly motivated researcher with a passion for HPC and a drive
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framework of the HAMburg Ocean Carbon Cycle model (HAMOCC) which is part of the ICON Earth system modeling framework remains technologically advanced and capable of efficiently exploiting emerging HPC