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Good knowledge of AI and applied Machine Learning Hands-on experience with High Performance Computing Systems Basic knowledge of System Architecture of Supercomputers and NVidia-GPUs Practical experience
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education in theory and practice of generative modeling, have research experience or education in life science data and have prior experience with remote GPU and HPC services. After the qualification
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communications. Evaluation of model performance can be conducted based on the data collected through the water tank. We have the GPU machines ($14k) to develop deep neural networks for underwater communications
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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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-fidelity simulation data will be used to build the machine-learning model to be then embedded into a GPU-accelerated blade-element momentum solver that will be made open-source to enable direct impact with
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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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measuring the environmental impact. This impact can be measured and calculated using our Software Energy Lab, which has multiple test machines with GPUs and, in the future, AI accelerators. Development teams
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calculated using our Software Energy Lab, which has multiple test machines with GPUs and, in the future, AI accelerators. Development teams currently lack guidance on how to create sustainable systems. You
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us to run large numerical simulations with billions grid points on mixed computer architectures including CPU and GPU machines. A current project is preparing the code set for the next generation of
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PhD candidate in the automated detection of measurable residual disease in hematological malignancie
-of-the-art compute & GPU infrastructure Collaborative and supportive research environment with expertise in both the generation (wetlab) and the analysis (drylab) of high-end cytometry data. Attendance