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Field
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
balance Training and international experience in public–private partnerships Mentoring for career development Access to high-performance computational resources (with GPUs) A collaborative environment
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balance Training and international experience in public–private partnerships Mentoring for career development Access to high-performance computational resources (with GPUs) A collaborative environment
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(OMOP CDM, FHIR) or metadata harmonisation Experience with ETL tools, workflow engines, or bigdata frameworks (e.g., Spark, NiFi, KNIME) Familiarity with containerisation (Docker) and HPC or GPU computing
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models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface of AI and genomics; prior experience with biological data
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made at the Postdoctoral Research Associate rank. The AI Postdoctoral Research Fellow will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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multiphase flows Your tasks Develop and extend the in-house GPU-accelerated multiphase Lattice Boltzmann (LBM) code for DNS-grade boiling multiphase flow related to nuclear reactor operation, including bubble
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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 3 months ago
and domain experts on highly scalable parallel applications with focus on: - development and implementation of parallel aplications, - GPU acceleration of applications, - application optimization