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language models from LLMs. Demonstrated publication record in the machine learning and AI field. Excellent programming and computer science skills. Preferred Qualification: Doctoral degree in electrical
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/Planning Internal Number: 6808728 Part Time Lecturer - Architecture About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling
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assistants and formalization of mathematics (e.g., Lean, Coq, Isabelle) Automated reasoning for mathematics (e.g., SAT/SMT solvers, first-order theorem provers) Machine learning for mathematics (e.g., model
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model
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role in defining system requirements and developing a robust AI framework to model and anticipate opponent behaviours and beliefs, leveraging state-of-the-art methods in machine learning and
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research background in or research experience with one or more of the following topics: Natural language processing & language modeling Machine learning & representation learning Interpretability and
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databases. Design, implementation, and testing of deep learning and AI algorithms for processing tabular, genomic and temporal data. Where to apply Website https://www.uam.es/uam/investigacion/ofertas-empleo
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency