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in high-performance computing using MPI. Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools. Experience in CFD meshing software. TU Delft (Delft University
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. in physics, bioengineering, or a closely related field. Preferred Qualifications Extensive experience in Monte Carlo simulation, GPU-based parallel computing, and image reconstruction techniques
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, managed directly by CARC staff, include multiple compute clusters that include state-of-the-art GPU resources, dedicated enterprise and high-performance storage systems, high-speed networking systems, and a
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, suitable for high I/O and large memory workloads. Mass Data Storage: 2.5 PB of networked storage, plus an additional 150 TB high-speed SSD storage for fast data access. GPU Supercomputing: A GPU server with
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, or emerging technologies); Python fluency plus a second server-side language; cloud & GPU proficiency; record of mentoring. Preferred – Master’s degree; expertise in TypeScript and good architectural
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, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data processing or willingness to learn quickly. Publications, thesis work, or demonstrable
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environment, which brings together more than 400 researchers across disciplines. The collaboration provides access to substantial computational resources (GPU nodes), advanced high-throughput instruments
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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configuration and management tools (e.g. SLURM, Ansible). Knowledge of or experience in networking systems, including DNS, HTTP, and TCP/IP. Experience with secure data processing and storage. Experience with
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signal processing and/or survey datasets. ML & AI techniques and applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering