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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 for scalable and green AI. Use cases in
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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of code to utilize GPU-acceleration on DTU’s high-performance computing cluster or other HPC systems. You will also analyze realistic physical implementations of the architectures you explore, with a
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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quantitative genetics, machine learning, bioinformatics, and population genetics, and their applications in an agricultural setting A modern dedicated computational infrastructure (CPUs & GPUs) Well-developed in
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be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working pipelines for computational design and the department’s bioimaging