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team as a PhD candidate to work on beyond the state-of-the-art model distillation and robustness methods, enabling efficient, reliable inference for challenging real-world problems in the semiconductor
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on what characterizes intelligence in humans and AI and develop psychometric methods and formal models to describe the similarities and differences? Research project entitled “How smart is AI really
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unreliable non-verbal cues. This project will investigate and formalize this tacit skill in the context of global supply chains where buyers are critically dependent on the knowledge and skills their suppliers
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properties. In this context, the CRN group recently reported a microfluidic method to bestow simple chemical coatings with the capacity to transduce, delay, and perturb chemical signals. Your project aims
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engineering, materials science, or a closely related field, with demonstrated interest or experience at the interface of computation and data driven methods. Experience with computational modelling techniques
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(algorithms), and statistics. During this project, you will develop new methods to construct phylogenetic networks and generalize mathematical frameworks of phylogenetic network classes to tackle related
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or transformed from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new
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from other maps. The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new answer
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level (neural network models) including plasticity. Electric fields will be estimated based on finite-element method models. The project can be partly adapted to your specific interests and your
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understand real-world challenges? Do you enjoy exploring and formally analyzing aspects of human reading and related cognitive tasks? Join us and contribute to an exciting multidisciplinary research project