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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
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driving systems, and new methods for fusing sound with other sensor data for more robust environment perception through deep learning. We offer a fully-funded 4-year PhD position at the Intelligent Vehicles
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. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485. Barocas, S., Hardt, M., & Narayanan, A. (2021). Fairness in Machine Learning. Retrieved from https
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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. We are looking for a highly motivated candidate who meets the following qualifications: A Master’s degree in Computer Science or Computer Engineering (completed before the start date of the PhD
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PhD Candidate in Exposomics, Machine Learning and Artificial Intelligence Faculty: Faculty of Veterinary Medicine Department: Department Population Health Sciences Hours per week: 36 to 40
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machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be
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statistics, together contributing to a deeper understanding of the basis of human brain connectivity and brain function. You have affinity with working with large datasets and have knowledge of data analysis
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and arms for both autonomous and prosthetic applications. If you’re excited by all this, we encourage you to apply. The opening: In this project, you will develop: Detailed, large-scale computer models