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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources. A competitive salary and benefits package, with relocation
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to leverage CPU and GPU cluster computing resources for large-scale image analysis. Train and mentor users on a variety of microscopy modalities, including confocal, STED, SIM, FLIM, TIRF, STORM, HCA, and
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astrophysical free boundaries. Responsibilities include running high-resolution GPU-accelerated simulations on exascale computing systems, developing and applying geometric measure theory tools to quantify
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pipelines for large-scale biological datasets. The Research Associate will: Design and implement high-performance workflows integrating GPU programming, deep learning, and large-scale data integration. Apply
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: – Knowledge of high-performance computers and GPU computing – Knowledge in data protection, especially with medical, personal data – Knowledge in operating ticket systems – Knowledge in creating wiki pages (e.g
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) VS Code (extensions for Python, Jupyter) Cloud Platforms & Remote Development Google Colab: Managing notebooks, using GPUs, data integration AWS / Azure / GCP basics (at least one of the three
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6G testbeds (indoor and outdoor) with GPU clusters and edge computing platforms Global Internet measurement infrastructure and satellite network access Opportunities to engage with Internet
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3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running
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molecules [doi.org/10.1021/jacs.2c07572 , doi.org/10.26434/chemrxiv-2023-5kl9x ]. (iii) Developing GPU-accelerated multireference methods to improve the accuracy and robustness of current state-of-the-art