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division 8.5 Planning, performing, and evaluating in-situ/4D computed tomography experiments Developing software for the quantitative evaluation of various image data sets (algorithms for detecting volume
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on the creation and application of predictive simulation models Collaboration on the development of data processing and fusion algorithms Collaboration on the virtual modeling of marine structures Conducting and
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
of this project requires the design, development, and training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus
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at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data, stochastic optimization
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computational approaches to uncover novel biomarkers and therapeutic strategies for CNS disorders. Key Responsibilities: Develop and implement algorithms for multimodal image fusion, combining data from MRI, PET
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About the Role We are seeking brilliant and passionate Algorithm Researchers to join our core team dedicated to advancing the frontiers of Artificial General Intelligence (AGI). In this role, you
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Gen AI investigators have developed 97 AI algorithms in clinical use, with more than 270 algorithms in development. To drive AI solutions, Mayo Clinic has partnered with Microsoft and NVIDIA to develop
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Professor positions in Quantum Mathematics with emphasis on pure mathematics with relations to quantum theory or with emphasis on Quantum algorithms, Quantum software and Quantum computing. The targeted
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algorithms, NLP models, and LLMs to analyze complex data. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems