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for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models. The subject works with
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, optimizing, and deploying AI models on HPC and GPU-based systems. Provide guidance on performance optimization, scaling, and efficient resource utilization. Contribute to architectural and design decisions in
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– Documented experience in large-scale data management, high-performance computing systems, GPU acceleration, and parallel file systems – Ability to communicate fluently in English, both spoken and written
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for Neural Rendering for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models
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of existing bioinformatic workflows and development of new pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation
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platforms, GPU environments or scientific computing. Experience in EU-funded projects or international collaborations. Experience working with learning management systems or digital learning tools. Knowledge
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
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and have prior experience with remote GPU and HPC services. After the qualification requirements, great emphasis will be placed on personal skills. Target degree: Doctoral degree Information regarding
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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linear algebra computations, building software for scientific applications using GPUs (Graphics Processing Unit), multi-threading and parallelism, numerical discretization methods (finite differences