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Reconfigurable/Spatial computing architectures, such as FPGAs, CGRAs, and AI accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs
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new nationwide AI system can be predicted using generated data sets of different sizes and measuring the environmental impact. This impact can be measured and calculated by our Software Energy Lab
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for tabular-native models. This can involve, for example, studying new TRL model architectures, serialization and tokenization techniques, among others. A strong interest and background in AI and/or NLP
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-powered data analysis. Publish reusable software and data artifacts where relevant. Communicate research outcomes through papers and talks at conferences, workshops, and beyond. Actively collaborate with