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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
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that employ novel virtual reality and robot-based paradigms. We are also leveraging several analytical tools such as computational modelling and machine learning. As a Research Assistant, you will help collect
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flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become increasingly popular in recent years. The recent introduction of OpenAI’s ChatGPT
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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel
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questions. The candidate should be organized, curious, analytically rigorous and comfortable learning unfamiliar methods. Good written and spoken English is essential. Research motivation: a clear interest in
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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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, neuroplasticity, and recovery from neurological and visual disorders. We're looking for candidates with a strong background in machine learning, deep learning, programming, and computational methods, combined with
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researchers from diverse backgrounds. Good communication skills and a willingness to learn are important for working effectively within and beyond the consortium. Candidates should hold a Master’s degree in
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interaction, geo-information science, or a related field, with experience in experimental research using virtual reality? If so, we have an exciting PhD opportunity for you! Spatial thinking plays a crucial