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robotics simulation environments across multiple platforms, including NVIDIA Isaac Sim, Gazebo, MuJoCo, and other relevant simulators for different applications and robotics platform Design modular and
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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storage and archiving solutions to collaboration and analytics tools. ARC also delivers Baskerville; a leading GPU accelerated National Compute Resource (NCR) and supports researchers using specialist
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conduct world-class applied research. We change and make a difference. Do you want to become one of us? The Department of Computer Science (DIDA) is one of three departments at the Faculty of Computing
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to the development of advanced language models and derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development
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and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures
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inhibitors with improved efficacy The project offers a highly interdisciplinary research environment spanning computational chemistry, cell biology, physics, and materials science. The work will leverage GPU
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biology results The project offers a highly interdisciplinary research environment spanning computational chemistry, neuroscience, molecular biology, and psychology. The work will leverage GPU computing
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of extensive omics data adaptation and application of methods from explainable artificial intelligence to understand gene regulation at different levels of abstraction adaptation and application of foundation
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storage and archiving solutions to collaboration and analytics tools. ARC also delivers Baskerville; a leading GPU accelerated National Compute Resource (NCR) and supports researchers using specialist