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-correction. This PhD falls into the fields of error-correction and Deep Learning. Due to the inherent unreliability of the DNA storage support, the goal will be to develop advanced deep learning models
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methods based on state-space models [3] have demonstrated strong capabilities in modeling very long sequences. In this context, these methods provide the perfect alternative to standard deep learning
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on advanced AI methodologies. Incorporating physics-guided deep learning models that explicitly integrate the underlying MRI signal formation process to enhance reconstruction reliability and interpretability
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computer science (notably from the artificial intelligence and deep-learning field), requiring the collaboration of experts with different expertise. The ambition of the project resides in popularizing AI
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an industrial track (2 years at IMT Atlantique + 12 months at Sony STC, Germany). 1.1. Domain and scientific/technical context Generative and creative systems based on Deep Learning have recently emerged under
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of Melbourne Physics department are pursuing a very fruitful collaboration around the exploitation of liquid xenon detectors to search for rare events in deep underground laboratories. In the context
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Rajput, Tim Widmayer, Ziyuan Shang, Maria Kechagia, Federica Sarro, and Tushar Sharma. 2024. Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement. ACM Trans. Softw. Eng
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networking protocols and AI. Understanding of networking protocols and architectures Strong background in ML and NLP, with hands-on experience in LLMs (e.g., GPT, BERT, LLaMA). Proficiency in deep learning
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of Melbourne Physics department are pursuing a very fruitful collaboration around the exploitation of liquid xenon detectors to search for rare events in deep underground laboratories. In the context