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Ryzen CPU, GPU, and NPU, in terms of inference speed, energy consumption, accuracy, and performance per watt. Different quantization levels (e.g., int8, fp16) will also be explored. Develop intelligent
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are a core member of IMEC, the world-leading research and innovation center in nanoelectronics and digital technologies. Our team is currently a fertile mix of people of different nationalities. The main
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy
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of advanced language models and 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
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-design with accelerators (FPGAs, GPUs, near-memory systems) to achieve real-time, energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI
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publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS or ICRA. You will have access to extensive compute resources at TU Delft, ranging from local GPU servers to large-scale HPC infrastructure
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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to the development of advanced language models and 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
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. Your tasks in detail: Become familiar with our previously developed neural network superstructure for learning iterative algorithms Extend the superstructure to tackle AC-PF problems of different
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are one of the largest, most international and most innovative employers in the region. With more than 6000 employees from 100 different countries, we are helping to build tomorrow's world every day