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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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questions. Given the uncertainties involved in food supply chains, we prefer candidates who have a background in (stochastic) optimization methods (e.g., machine learning, stochastic dynamic programming
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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provide a creative, nurturing campus environment where our students can become the best individuals possible, learn from the best and brightest faculty, and make a positive difference in the community
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hold an MSc degree in environmental science or ecology, with a proven expertise in data analysis, organizing and handling. Expertise in machine learning is a plus. A sound command of the English language
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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intersection of mechanics, materials, and machine learning. Collaboration with international experts from diverse disciplines. Access to cutting-edge computational and experimental facilities. Supervision of MSc
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includes PhD students and researchers at TU Delft, Wageningen University and Research, and Erasmus University Rotterdam, as well as industrial partners that specialize in machine learning, climate control
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simulations. Job Description Are you passionate about bridging computational modeling with clinical cardiology to solve real-world healthcare challenges? We're seeking a PhD candidate to develop innovative