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for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in Electrical and Electronic Engineering, Applied
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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to improve software quality, testability, and fault tolerance. Manage multiple projects simultaneously, supporting faculty and postdoctoral researchers in achieving their research objectives. Collaborate with
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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& Medicine, Scientific Discovery, Engineered Systems, Security & Safety, and People, Policy & Governance. DSAI seeks multiple Research Software Engineers with strong academic backgrounds and relevant
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at MGHPCC facility (i.e., data center, compute, storage, networking, and other core capabilities). Deploy, monitor, and manage CPUs, GPUs, storage, file systems, networking on HPC systems. Develop and deploy
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skill set, willing to work with new technologies and signal processing and propagation modeling techniques, be highly organized and capable of planning and coordinating multiple tasks and managing
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) general-purpose hardware such as accelerators for AI and ML, high-performance computing, low-power edge computing, quantum computing, cybersecurity, chiplets, and CPU, TPU, GPU, and FPGA systems; or (2
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contributing to multiple projects including resilience-aware scheduling, deep learning workload job scheduling, and storage system performance tuning. The candidate will have the opportunity to engage in
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of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and