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Field
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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datasets generated by the Phenomobile.v2+ to identify key traits affecting crop performance under stress conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU
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emphasis on programmability and the characterization of AI capabilities in CPUs, GPUs, and dedicated accelerators; Identification of computational patterns suited to AI-enhanced processors and standalone
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or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design Previous collaboration with experimental biologists
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. Zou, which includes access to high performance computational resources with GPUs, conference travel support, and great opportunities for collaboration and networking with experts in Industrial
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification