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Field
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projects grounded in data analysis in the domain of computational pediatric cancer research. They will develop and support data processing and analysis tools, create new algorithms for focused research
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numerical tensor network algorithms, and their application in the study of strongly correlated quantum matter. The successful candidate is able to actively contribute to the research profile of both the
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in sampling algorithms and related areas (for example, efficient sampling from the Gibbs measure, Markov Chain Monte Carlo methods, etc.). Experience in distributed or parallel computing is a plus
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of Hope National Medical center under Dr. Supriyo Bhattacharya. Our mission is to advance the next generation of in-silico algorithms for understanding diabetes and related disease mechanisms and
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, data science algorithms, scientific and numerical computation. Familiarity with common Python tools including Pandas, Numpy, Scipy, Django, RDKit. Proficiency in coding and debugging in Pytho. Strong
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring
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reliability are as important as high accuracy Supervise student projects at BSc, MSc, and PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring
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with data-driven models (hybrid modeling) for improved accuracy and generalization. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process