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Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable digital CIM-based AI
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. The Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable analog CIM-based AI
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PhD in biology, cognitive science, or an adjacent field (e.g. biomedicine, computer sciences); A strong academic track record, including high-quality publications (quantity is less important
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will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models for phenotype prediction, helping to accelerate the cell
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cell lines have been engineered and characterised, you will image them using a variety of microscopy methods, and collaborate with a team of computer vision scientists to build ML-based models
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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profile: The ideal candidate will have: A PhD in biology, cognitive science, or an adjacent field (e.g. biomedicine, computer sciences); A strong academic track record, including high-quality publications
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qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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degree in a relevant field (e.g., transportation, human-computer interaction, cognitive science, mechanical engineering, or related disciplines). Solid background in XR technologies and skills in