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through a self-learning chip prototype, improving performance and durability in automotive applications. Specifically, this PhD project focuses on memristive materials as electronic realizations
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, artificial intelligence and machine learning and/or computational modelling approaches. Moreover, the candidate will have a leading role in expanding and professionalizing the growing computational biology
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maintain robustness through evolution using live-cell imaging and multiscale modelling. Job description Cells are often described as intricate machines where proteins work together in a tightly coordinated
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. Main areas of interest are source coding, channel coding, multi-user information theory, security, and machine learning. We typically use information-theoretical frameworks to model the scenarios under
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described as intricate machines where proteins work together in a tightly coordinated fashion to produce essential cellular functions. Yet evolution challenges this picture. Proteins that are crucial for a
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electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour
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such as data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant to the description of the tasks outlined above. Additional requirements In
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8 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model