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                : Have (or be close to completing) an MSc in computer science or a related field. Have an interest in both theory and practice. Have strong programming skills and interest in developing state-of-the-art 
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                for Logic, Language and Computation (ILLC) at the University of Amsterdam is inviting applications for a fully funded PhD position in the NWO project "Simplicial type theory" lead by dr. Benno van den Berg 
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                Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Combine theory, modeling, and experiments to uncover how 
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                trade-offs and maximizes synergies between landscape services. In this PhD project you will develop this innovative theory and combine spatial analysis with a Research Through Design approach. You first 
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                are looking for highly motivated and curious PhD candidates who possess relevant expertise in (the theory & methods of) cognitive and experimental psychology, statistical modeling and data science, and open 
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                control theory? Are you interested in working with next generation high-tech systems? The main challenge lies in the reliable operation of the entire system, where many interconnected modules affect each 
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                adaptation and learning from their experiences. Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements 
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                theory to unlock the efficiency of neuromorphic compute in multi-timescale processing. Job description Neuromorphic computing promises new energy efficiency records by taking inspiration from biology 
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                ://www.academictransfer.com/en/jobs/354978/phd-position-theory-of-learning… Requirements Specific Requirements You hold an MSc in physics, engineering physics or mathematics. You have a good command of analytical techniques 
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                using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as well as programming to test out the methods and