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Field
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Privacy-Preserving and Reliable Artificial Intelligence to be occupied starting from the 01.10.2022. The Trustworthy and Privacy-Preserving AI Group (PI: PD Dr. Georgios Kaissis) at the Institute for AI in
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Such structures can be used to construct artificial cell mimics and new materials. A scaffold-free DNA tile assembly is a programmable method for the formation of two- and three-dimensional DNA structures which
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? Then you might be the PhD candidate we are looking for! Join ANTICIPATE (Artificial Intelligence and Epidemic Modeling to Prepare Hospitals for the Next Respiratory Pathogen with Pandemic Potential), a
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. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming
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arbitrary complexity and shape at the sub-nanometer scale. Such structures can be used to construct artificial cell mimics and new materials. A scaffold-free DNA tile assembly is a programmable method for the
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Artificial Intelligence – ScaDS.AI. We seek to fill the above position as soon as possible (e.g., Oktober 1, 2025). This interdisciplinary project investigates how physical systems can serve as computational
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(or nearly completed) Master's degree in Human-Computer Interaction, Artificial Intelligence, Cognitive Science, or a related discipline. Knowledge of and preferably demonstrated experience with human
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As part of the German government's artificial intelligence (AI) strategy, the successful Saxon competence center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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sciences and artificial intelligence, and translate your findings to improve human health? Are you excited to develop and use machine learning approaches to gain new understanding of the molecular physiology