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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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, as well as the way memory, trauma and history are entangled. Job description: In close coordination with the project management and the other project members, the computer-assisted analysis
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies
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, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will: Develop and apply machine
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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of the project InsAIderKnowledge funded by the Georg-Nemetschek-Institute for the duration of 48 Months. The candidate has the opportunity to pursue a doctoral degree (PhD). Renumeration is 100% TVL E13 according
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dynamics, data science, and machine learning are beneficial. Please submit your detailed application with the usual documents by August 15, 2025 (stamped arrival date of the university central mail
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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team