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and Data-Driven Discovery, which involves creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection. The second pillar
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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at: www.fz-juelich.de/gp/Careers_Docs Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We
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sheet evolution, methane hydrate fluxes, or applying machine learning to geosciences to reconstruct glacial histories and project future ice sheet behavior. Please read this interview for more details
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conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
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of the following topics: data collection, information extraction using large language models (LLMs), machine learning (ML), natural language processing (NLP) Expertise in software development with
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contributions in one or more of the following key areas: computational modeling of chemical systems, AI-driven materials discovery/design, robotics for chemical synthesis, machine learning applications in
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data. Develop and apply machine learning models to estimate uncertainty in climate impact statements. Analyse spatial and temporal patterns and trends in climate-extreme impacts. Cross-validate
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· Introduction to Biomedical Informatics · Leadership and Innovation for Informatics · Concepts in Computer Programming · Ethics and Policy Questions: Genomics, Healthcare and Big Data
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, cell biology, time-lapse microscopy and single cell analysis. You will work in a dynamic and highly interdisciplinary team including computer scientists, experimentalists and clinicians. You will be