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Field
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inference. For Position 1, experience with LSS simulations and analysis, and ideally galaxy redshift surveys and HPC. For Position 2, experience with gamma-ray data analysis and its astrophysical
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extreme weather forecasting, employing data science models as developed in computer vision and natural language processing (NLP). This project leverages the large-scale high-performance computing (HPC
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acquired in situ during UXO disposal campaigns. This multidisciplinary project gathers experts in underwater acoustics, seismology, geology, and HPC modeling, from 3 French partners, namely Quiet-Oceans (PI
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, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC infrastructure and large
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leading role in detector operations and in major upgrade efforts, including the ITk Pixel detector and TDAQ systems. The group is also actively advancing work in high-performance computing (HPC) and machine
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Interactions external link groups, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC
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architectures. Knowledge of job scheduling tools such as Slurm or PBS for submitting and monitoring large-scale simulations. - High-Performance Computing (HPC): Proficiency in high-performance computing to run
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publication record and experience analyzing large-scale genomic datasets in HPC environments. Applicants must be within 5 years post receipt of their PhD. Terms of employment include a competitive salary and
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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or expected to be completed within the next six months. Experience in ecological modelling and/or Land Surface Modelling Knowledge and experience working in HPC environment. Programming experience in Fortran