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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You will gain
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experience aligned to the goals of at least one of the Centre for Data Science and AI’s with commensurate output. E2 Substantial experience in machine learning and AI, including experience in machine learning
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, as well as other sustainability relevant endeavours Integrating advanced machine learning methods in thermodynamics for computer-aided property predictions, molecular and product design and
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this diversity. Our research spans comparative genomics, chromatin architecture, gene expression, protein abundance, and metabolite profiling—combined with computational biology, machine learning, and advanced
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (https://www.ntnu.edu/nv/phd) PLEASE NOTE: For detailed information
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, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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, as well as other sustainability relevant endeavours Integrating advanced machine learning methods in thermodynamics for computer-aided property predictions, molecular and product design and
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focus on the dynamic nature of phase transitions in APIs, using machine learning interatomic potentials (MLIPs) to construct force fields whose mathematical complexity will be carefully controlled in