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/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 2 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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-scale training of a high-performance foundation model using a dedicated GPU cluster Fine-tuning the pretrained model on real-world health data from lifespin’s proprietary database Collaborating closely
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foundational questions. To benefit from this revolution in full, it is of utmost importance to influence the related development with a highly ambitious scientific programme underpinned by robust Exascale-ready
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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Program for Data-Driven Life Science (DDLS ) and the student joins its research program . Supervision: Associate Professor Hossein Azizpour What we offer Admission requirements To be admitted