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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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research interests might be considered, priority will be given to those able to relate to one or more of the above topics. Applicants must have a PhD in Mathematics, Statistics or Computer Science obtained
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collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include Expertise in several areas among
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Information Theory. While excellent candidates with other research interests might be considered, priority will be given to those able to relate to one or more of the above topics. Applicants must have a PhD in
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills
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candidates will work in a multidisciplinary Center environment with world-class research infrastructure, consisting of PhD-level scientists, graduate and undergraduate students. The positions are funded
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of PhD), or Research Associate (more than 3 years of PhD). A strong preference is for individuals with (a) computer science or computer engineering degrees with previous experience in natural