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Field
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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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to work on meaningful projects with direct clinical relevance. About the role In this role, you will develop and implement computer vision and deep learning algorithms to analyse CT and MRI data from
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We are seeking to recruit a motivated postdoctoral researcher in Verification and Game Theory. You will enjoy developing algorithms, lower bounds, and possibly proof-of-concept implementations
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control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance the safety and robustness of human-robot interaction. The successful applicant will engage
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key challenges in
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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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-seeking algorithms in transportation networks Game-theoretic approaches to cybersecurity and security games Integration of human behavior modeling in smart mobility systems Required Qualifications
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs