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Field
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animation tools, and GPU-based high-performance computing at MPI. You will also be embedded in a rich theoretical and computational environment supported by the Multimodal Language Department.Requirements
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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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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. 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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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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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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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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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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for early detection, differential diagnosis, progression monitoring, and treatment design. Key attractions are access to a high-performance computing cluster, NUS HPC (H100/H200 GPU clusters), two 3T Prisma
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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