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Field
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and GPU servers for the delivery of the PC exercises, and jointly supervising the PC exercises during the course What you contribute Student on a STEM degree programme Good knowledge of at least one of
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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken
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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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documentation of HPC architectures, configurations, and operational procedures. Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects
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of existing bioinformatic workflows and development of new pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy
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is of advantage: Knowledge of parallel programming and HPC architectures, including accelerators (e.g., GPUs) Experience in modelling and simulation, ideally in the field of energy systems Experience
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. This is not a remote position. Additional Information Competitive compensation package with attractive work conditions. Access to state-of-the-art research facilities and GPU cluster. Opportunities
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-dimensional biological datasets. Familiarity with GPU computing and high-performance computing (HPC) environments. Other Requirements Ability to work collaboratively with researchers across computational and
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optimizing compilers, the classical and quantum fragments are separated in efficient implementations adapted to the changing QPUs and GPUs architectures. The candidate will work at the intersection