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modeling of quantum computer systems. This can be either a holder of a PhD in computer science and/or engineering (computer architecture and HPC related topics) with knowledge in quantum computing or a PhD
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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 2 months ago
Master's degree, Engineer's degree, or PhD in computer science to join a team responsible for the packaging, deployment, and testing of software libraries for high-performance computing (HPC). This position
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if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no/studier/phet) You must
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Research Scientist IV, Information Science (Extended Temporary) (Remote Work Available) Posting Number req25414 Department Information Science Department Website Link https://infosci.arizona.edu/about
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science » Computer systems Medical sciences » Other Physics » Optics Architecture » Other Researcher Profile Leading Researcher (R4) Positions PhD Positions Country Spain Application Deadline 21 Jan 2026 - 23:59
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, Blockchain, Cloud Computing, Software Engineering, as well as Data Architecture & Engineering. In this role, the Teaching Faculty will report directly to the MGEN Executive Program Director. The selected
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. Describe a deep learning project you have executed—ideally a creative use of a vision transformer, U-Net architecture, or Diffusion model that you trained yourself. Projects in computer vision for microscopy
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use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for microscopy image analysis are especially relevant. Include a link
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Enhancement of AI/ML with in-network computing & processing Adaptation & optimization of AI/ML software libraries for non-conventional hardware architectures Physics-informed ML surrogates for efficient