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Field
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Job Description Field of Research: Computational biology/bioinformatics with emphasis on spatial transcriptomics, proteomics, and immunosequencing using Pixel‑seq and an immune receptor‑focused
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for these architectures remains a key challenge. This project will explore how reconfigurable/spatial computing devices can be integrated into large-scale heterogeneous infrastructures, which include CPUs, GPUs, DPUs, and
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skills in Python and/or R; experience with Linux/HPC environments is an advantage Experience with genomic data analysis, high-performance computing, GPU programming, or software development is a plus
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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. Proficiency with cloud platforms such as Azure, AWS, or GCP, including GPU-based compute environments. Experience with monitoring and observability tools (e.g., dashboards, metrics collection, alerting systems
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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compute nodes and over 38,000 CPU cores, over 200 GPUs, 200 TB of memory, 2PB of high-speed data storage, and high-speed networking. The Meteorology program is co-located with the Iowa Environmental Mesonet
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%). You will work on the extension of the DUNE-FEM package to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering
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, PyTorch/TensorFlow) experience working with cluster/GPU computing resources is desirable excellent verbal and written communication skills and the ability to work effectively in a collaborative team
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theorists. The overview of KMI and members can be found at http://www.kmi.nagoya-u.ac.jp/ ; Requirements: A PhD (or close to completion) in physics or related disciplines, such as astronomy, computer