
11 - 50 employees
π Education
π€ Artificial Intelligence
π₯ Healthcare
Education β’ Artificial Intelligence β’ Healthcare
JHU EEHPC is the Energy Efficient High Performance Computing Lab at Johns Hopkins University, led by Tinoosh Mohsenin. The lab focuses on designing medical, health, and wearable devices and developing tiny, energy-efficient wearable and mobile computing systems through cross-layer approaches spanning algorithms, architecture, hardware, and system integration. It applies high-performance computing and artificial intelligence methods to smart health monitoring, robotics, autonomous navigation, and related domains, aiming to revolutionize healthcare, transportation, and public utilities.
π₯ 8 minutes ago
π¦ Maryland β Remote
π΅ $85.5k - $149.8k / year
β° Full Time
π‘ Mid-level
π Senior
π Research Engineer
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11 - 50 employees
π Education
π€ Artificial Intelligence
π₯ Healthcare
Education β’ Artificial Intelligence β’ Healthcare
JHU EEHPC is the Energy Efficient High Performance Computing Lab at Johns Hopkins University, led by Tinoosh Mohsenin. The lab focuses on designing medical, health, and wearable devices and developing tiny, energy-efficient wearable and mobile computing systems through cross-layer approaches spanning algorithms, architecture, hardware, and system integration. It applies high-performance computing and artificial intelligence methods to smart health monitoring, robotics, autonomous navigation, and related domains, aiming to revolutionize healthcare, transportation, and public utilities.
β’ Support faculty, researchers, and students engaged in high-performance and AI-driven research. β’ Deploy, optimize, and maintain scientific software and computational workflows on advanced HPC Systems and related infrastructure. β’ Manage and troubleshoot complex software stacks, containerized applications, and GPU-accelerated workloads using tools such as SLURM, Easy build, Spack, etc. β’ Collaborate closely with interdisciplinary research groups to enhance system performance, streamline data-intensive workflows, and integrate cutting-edge technologies. β’ Analyze and optimize the performance of AI models and HPC applications, focusing on GPU-enabled computing. β’ Implement parallel processing, distributed computing, and resource management techniques for efficient job execution. β’ Develop, debug, and maintain software tools, libraries, and frameworks supporting HPC and AI workloads. β’ Manage and support scientific software deployment across HPC, cloud-based, and colocation facilities.
β’ Masterβs degree in computer science or a closely related quantitative discipline. β’ Five years of experience in HPC user support, software deployment, and performance optimization within an academic or research environment. β’ Experience in scientific computing environments and applications. β’ Hands-on experience with SLURM, for job scheduling. β’ Proficiency in Python, Perl, C/C++, and Shell scripting for automation and system management. β’ Advanced knowledge of Linux systems and proficiency in scripting languages such as Python, Perl, and Shell. β’ Familiarity with scientific application management tools such as Containerization, LUA modules, CMake, Spack, and EasyBuild. β’ Training Workshops, Performance Optimization and Troubleshooting.
β’ Total rewards package that supports our employees' health, life, career and retirement.
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